From 7e0cfcc7b4f4230a7a6c6469773510787b29d9c5 Mon Sep 17 00:00:00 2001
From: pat-alt <altmeyerpat@gmail.com>
Date: Fri, 31 Mar 2023 12:48:04 +0200
Subject: [PATCH] ufff

---
 .DS_Store                                     |  Bin 0 -> 8196 bytes
 .../proposal/execute-results/html.json        |    4 +-
 artifacts/mnist_vae.jls                       |  Bin 1003702 -> 1003702 bytes
 artifacts/mnist_vae_weak.jls                  |  Bin 199710 -> 199710 bytes
 bib.bib                                       | 2750 +++++++++++++++++
 docs/index.html                               |  426 +++
 docs/notebooks/proposal.html                  |  601 ++++
 docs/site_libs/bootstrap/bootstrap-icons.css  | 2018 ++++++++++++
 docs/site_libs/bootstrap/bootstrap-icons.woff |  Bin 0 -> 164168 bytes
 docs/site_libs/bootstrap/bootstrap.min.css    |   10 +
 docs/site_libs/bootstrap/bootstrap.min.js     |    7 +
 docs/site_libs/clipboard/clipboard.min.js     |    7 +
 docs/site_libs/quarto-html/anchor.min.js      |    9 +
 docs/site_libs/quarto-html/popper.min.js      |    6 +
 .../quarto-syntax-highlighting.css            |  203 ++
 docs/site_libs/quarto-html/quarto.js          |  884 ++++++
 docs/site_libs/quarto-html/tippy.css          |    1 +
 docs/site_libs/quarto-html/tippy.umd.min.js   |    2 +
 docs/site_libs/quarto-nav/headroom.min.js     |    7 +
 docs/site_libs/quarto-nav/quarto-nav.js       |  273 ++
 .../quarto-search/autocomplete.umd.js         |    3 +
 docs/site_libs/quarto-search/fuse.min.js      |    9 +
 docs/site_libs/quarto-search/quarto-search.js | 1140 +++++++
 notebooks/Manifest.toml                       |   24 +-
 notebooks/Project.toml                        |    2 +
 notebooks/intro.qmd                           |   12 +-
 .../proposal_files/execute-results/html.json  |    4 +-
 paper/paper.pdf                               |  Bin 303523 -> 303523 bytes
 src/CCE.jl                                    |    3 +
 src/model.jl                                  |    8 -
 src/penalties.jl                              |    2 +-
 31 files changed, 8393 insertions(+), 22 deletions(-)
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 create mode 100644 docs/index.html
 create mode 100644 docs/notebooks/proposal.html
 create mode 100644 docs/site_libs/bootstrap/bootstrap-icons.css
 create mode 100644 docs/site_libs/bootstrap/bootstrap-icons.woff
 create mode 100644 docs/site_libs/bootstrap/bootstrap.min.css
 create mode 100644 docs/site_libs/bootstrap/bootstrap.min.js
 create mode 100644 docs/site_libs/clipboard/clipboard.min.js
 create mode 100644 docs/site_libs/quarto-html/anchor.min.js
 create mode 100644 docs/site_libs/quarto-html/popper.min.js
 create mode 100644 docs/site_libs/quarto-html/quarto-syntax-highlighting.css
 create mode 100644 docs/site_libs/quarto-html/quarto.js
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 create mode 100644 docs/site_libs/quarto-nav/quarto-nav.js
 create mode 100644 docs/site_libs/quarto-search/autocomplete.umd.js
 create mode 100644 docs/site_libs/quarto-search/fuse.min.js
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-    "markdown": "---\ntitle: High-Fidelity Counterfactual Explanations through Conformal Prediction\nsubtitle: Research Proposal\nabstract: |\n    We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n---\n\n\n\n## Motivation\n\nCounterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n\n### Counterfactual Explanations or Adversarial Examples?\n\nMost state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n\n\n\n\n\n![You may not like it, but this is what stripped-down counterfactuals look like. Here we have used @wachter2017counterfactual to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).](www/you_may_not_like_it.png){#fig-adv}\n\nThe crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nIn the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n\n![Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).](www/mnist_9to4_latent.png){#fig-latent}\n\n> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n\n- Latent can be manipulated: \n    - train biased model\n    - train VAE with biased variable removed/attacked (use Boston housing dataset)\n    - hypothesis: will generate bias-free explanations\n\n### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n\nIn light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n\n::: {#prp-surrogate}\n\n## Avoid Surrogates\n\nSince we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n\n:::\n\nIn cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n\nIn the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n\nDoes fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n\n::: {#def-fidele}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nIn order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n\n1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n\nIf the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n\n> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n\n## Conformal Counterfactual Explanations\n\nIn @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n\n### Minimizing Predictive Uncertainty\n\n@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n\n- Problem: restricted to Bayesian models.\n- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n\n### Background on Conformal Prediction\n\n- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n- Conformal prediction is instance-based. So is CE. \n- Take any fitted model and turn it into a conformal model using calibration data.\n- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n\n> Does the coverage guarantee carry over to counterfactuals?\n\n### Generating Conformal Counterfactuals\n\nWhile Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n\n> Challenge: still need to implement these loss functions. \n\n## Experiments\n\n### Research Questions\n\n- Is CP alone enough to ensure realistic counterfactuals?\n- Do counterfactuals improve further as the models get better?\n- Do counterfactuals get more realistic as coverage\n- What happens as we vary coverage and setsize?\n- What happens as we improve the model robustness?\n- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n\n- Benchmarking:\n    - add PROBE [@pawelczyk2022probabilistically] into the mix.\n    - compare travel costs to domain shits.\n\n> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n\n## References\n\n",
+    "markdown": "---\ntitle: High-Fidelity Counterfactual Explanations through Conformal Prediction\nsubtitle: Research Proposal\nabstract: |\n    We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n---\n\n\n\n## Motivation\n\nCounterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n\n### Counterfactual Explanations or Adversarial Examples?\n\nMost state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n\n\n\n\n\n\n\n![You may not like it, but this is what stripped-down counterfactuals look like. Here we have used @wachter2017counterfactual to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).](www/you_may_not_like_it.png){#fig-adv}\n\nThe crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nIn the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n\n\n\n\n\n\n\n![Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).](www/mnist_9to4_latent.png){#fig-latent}\n\n> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n\n- Latent can be manipulated: \n    - train biased model\n    - train VAE with biased variable removed/attacked (use Boston housing dataset)\n    - hypothesis: will generate bias-free explanations\n\n### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n\nIn light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n\n::: {#prp-surrogate}\n\n## Avoid Surrogates\n\nSince we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n\n:::\n\nIn cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n\nIn the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n\nDoes fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n\n::: {#def-fidele}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nIn order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n\n1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n\nIf the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n\n> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n\n## Conformal Counterfactual Explanations\n\nIn @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n\n### Minimizing Predictive Uncertainty\n\n@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n\n- Problem: restricted to Bayesian models.\n- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n\n### Background on Conformal Prediction\n\n- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n- Conformal prediction is instance-based. So is CE. \n- Take any fitted model and turn it into a conformal model using calibration data.\n- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n\n> Does the coverage guarantee carry over to counterfactuals?\n\n### Generating Conformal Counterfactuals\n\nWhile Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n\n> Challenge: still need to implement these loss functions. \n\n## Experiments\n\n### Research Questions\n\n- Is CP alone enough to ensure realistic counterfactuals?\n- Do counterfactuals improve further as the models get better?\n- Do counterfactuals get more realistic as coverage\n- What happens as we vary coverage and setsize?\n- What happens as we improve the model robustness?\n- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n\n- Benchmarking:\n    - add PROBE [@pawelczyk2022probabilistically] into the mix.\n    - compare travel costs to domain shits.\n\n> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n\n## References\n\n",
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U1pzn#w|*7^FZGug2LZ+bP0)%JlmGw#

delta 62
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diff --git a/bib.bib b/bib.bib
new file mode 100644
index 00000000..d0451278
--- /dev/null
+++ b/bib.bib
@@ -0,0 +1,2750 @@
+@Online{mw2023fidelity,
+  author       = {Merriam-Webster},
+  title        = {"Fidelity"},
+  url          = {https://www.merriam-webster.com/dictionary/fidelity},
+  language     = {en},
+  organization = {Merriam-Webster},
+  urldate      = {2023-03-23},
+  abstract     = {the quality or state of being faithful; accuracy in details : exactness; the degree to which an electronic device (such as a record player, radio, or television) accurately reproduces its effect (such as sound or picture)… See the full definition},
+}
+
+@InProceedings{altmeyer2023endogenous,
+  author    = {Altmeyer, Patrick and Angela, Giovan and Buszydlik, Aleksander and Dobiczek, Karol and van Deursen, Arie and Liem, Cynthia},
+  booktitle = {First {IEEE} {Conference} on {Secure} and {Trustworthy} {Machine} {Learning}},
+  title     = {Endogenous {Macrodynamics} in {Algorithmic} {Recourse}},
+  file      = {:altmeyerendogenous - Endogenous Macrodynamics in Algorithmic Recourse.pdf:PDF},
+  year      = {2023},
+}
+
+%% This BibTeX bibliography file was created using BibDesk.
+%% https://bibdesk.sourceforge.io/
+
+%% Created for Patrick Altmeyer at 2022-12-13 12:58:22 +0100 
+
+
+%% Saved with string encoding Unicode (UTF-8) 
+
+
+
+@Article{abadie2002instrumental,
+  author        = {Abadie, Alberto and Angrist, Joshua and Imbens, Guido},
+  title         = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
+  number        = {1},
+  pages         = {91--117},
+  volume        = {70},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica : journal of the Econometric Society},
+  shortjournal  = {Econometrica},
+  year          = {2002},
+}
+
+@Article{abadie2003economic,
+  author        = {Abadie, Alberto and Gardeazabal, Javier},
+  title         = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
+  number        = {1},
+  pages         = {113--132},
+  volume        = {93},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {American economic review},
+  year          = {2003},
+}
+
+@InProceedings{ackerman2021machine,
+  author        = {Ackerman, Samuel and Dube, Parijat and Farchi, Eitan and Raz, Orna and Zalmanovici, Marcel},
+  booktitle     = {2021 {{IEEE}}/{{ACM Third International Workshop}} on {{Deep Learning}} for {{Testing}} and {{Testing}} for {{Deep Learning}} ({{DeepTest}})},
+  title         = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}},
+  pages         = {1--8},
+  publisher     = {{IEEE}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Article{allen2017referencedependent,
+  author        = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
+  title         = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
+  number        = {6},
+  pages         = {1657--1672},
+  volume        = {63},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Management Science},
+  year          = {2017},
+}
+
+@Article{altmeyer2018option,
+  author        = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
+  title         = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2018},
+}
+
+@Article{altmeyer2021deep,
+  author        = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
+  title         = {Deep {{Vector Autoregression}} for {{Macroeconomic Data}}},
+  url           = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
+  bdsk-url-1    = {https://thevoice.bse.eu/wp-content/uploads/2021/07/ds21-project-agusti-et-al.pdf},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Book{altmeyer2021deepvars,
+  author        = {Altmeyer, Patrick},
+  title         = {Deepvars: {{Deep Vector Autoregession}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Misc{altmeyer2022counterfactualexplanations,
+  author        = {Altmeyer, Patrick},
+  title         = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
+  url           = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+  bdsk-url-1    = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2022},
+}
+
+@Software{altmeyerCounterfactualExplanationsJlJulia2022,
+  author        = {Altmeyer, Patrick},
+  title         = {{{CounterfactualExplanations}}.Jl - a {{Julia}} Package for {{Counterfactual Explanations}} and {{Algorithmic Recourse}}},
+  url           = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+  version       = {0.1.2},
+  bdsk-url-1    = {https://github.com/pat-alt/CounterfactualExplanations.jl},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2022},
+}
+
+@Unpublished{angelopoulos2021gentle,
+  author        = {Angelopoulos, Anastasios N. and Bates, Stephen},
+  title         = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2107.07511},
+  eprinttype    = {arxiv},
+  file          = {:/Users/FA31DU/Zotero/storage/RKSUMYZG/Angelopoulos and Bates - 2021 - A gentle introduction to conformal prediction and .pdf:;:/Users/FA31DU/Zotero/storage/PRUEKRR3/2107.html:},
+  year          = {2021},
+}
+
+@Misc{angelopoulos2022uncertainty,
+  author        = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
+  title         = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
+  eprint        = {2009.14193},
+  eprinttype    = {arxiv},
+  abstract      = {Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network's probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90\%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller than a stand-alone Platt scaling baseline.},
+  archiveprefix = {arXiv},
+  bdsk-url-1    = {http://arxiv.org/abs/2009.14193},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  file          = {:/Users/FA31DU/Zotero/storage/5BYIRBR2/Angelopoulos et al. - 2022 - Uncertainty Sets for Image Classifiers using Confo.pdf:;:/Users/FA31DU/Zotero/storage/2QJAKFKV/2009.html:},
+  keywords      = {Computer Science - Computer Vision and Pattern Recognition, Mathematics - Statistics Theory, Statistics - Machine Learning},
+  month         = sep,
+  number        = {arXiv:2009.14193},
+  primaryclass  = {cs, math, stat},
+  publisher     = {{arXiv}},
+  year          = {2022},
+}
+
+@Article{angelucci2009indirect,
+  author        = {Angelucci, Manuela and De Giorgi, Giacomo},
+  title         = {Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?},
+  number        = {1},
+  pages         = {486--508},
+  volume        = {99},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {American economic review},
+  year          = {2009},
+}
+
+@Article{angrist1990lifetime,
+  author        = {Angrist, Joshua D},
+  title         = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records},
+  pages         = {313--336},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The American Economic Review},
+  year          = {1990},
+}
+
+@Unpublished{antoran2020getting,
+  author        = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
+  title         = {Getting a Clue: {{A}} Method for Explaining Uncertainty Estimates},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2006.06848},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Article{arcones1992bootstrap,
+  author        = {Arcones, Miguel A and Gine, Evarist},
+  title         = {On the Bootstrap of {{U}} and {{V}} Statistics},
+  pages         = {655--674},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Annals of Statistics},
+  year          = {1992},
+}
+
+@Article{ariely2003coherent,
+  author        = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+  title         = {``{{Coherent}} Arbitrariness'': {{Stable}} Demand Curves without Stable Preferences},
+  number        = {1},
+  pages         = {73--106},
+  volume        = {118},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Quarterly journal of economics},
+  year          = {2003},
+}
+
+@Article{ariely2006tom,
+  author        = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+  title         = {Tom {{Sawyer}} and the Construction of Value},
+  number        = {1},
+  pages         = {1--10},
+  volume        = {60},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Economic Behavior \& Organization},
+  year          = {2006},
+}
+
+@Article{arrieta2020explainable,
+  author        = {Arrieta, Alejandro Barredo and Diaz-Rodriguez, Natalia and Del Ser, Javier and Bennetot, Adrien and Tabik, Siham and Barbado, Alberto and Garcia, Salvador and Gil-Lopez, Sergio and Molina, Daniel and Benjamins, Richard and others},
+  title         = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
+  pages         = {82--115},
+  volume        = {58},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Information Fusion},
+  year          = {2020},
+}
+
+@Article{auer2002finitetime,
+  author        = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
+  title         = {Finite-Time Analysis of the Multiarmed Bandit Problem},
+  number        = {2},
+  pages         = {235--256},
+  volume        = {47},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Machine learning},
+  year          = {2002},
+}
+
+@Article{barabasi2016network,
+  author        = {Barab{\'a}si, Albert-L{\'a}szl{\'o}},
+  title         = {Network {{Science}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Network Science},
+  year          = {2016},
+}
+
+@Unpublished{bastounis2021mathematics,
+  author        = {Bastounis, Alexander and Hansen, Anders C and Vla{\v c}i{\'c}, Verner},
+  title         = {The Mathematics of Adversarial Attacks in {{AI}}--{{Why}} Deep Learning Is Unstable despite the Existence of Stable Neural Networks},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2109.06098},
+  eprinttype    = {arxiv},
+  year          = {2021},
+}
+
+@Article{bechara1997deciding,
+  author        = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
+  title         = {Deciding Advantageously before Knowing the Advantageous Strategy},
+  number        = {5304},
+  pages         = {1293--1295},
+  volume        = {275},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Science (New York, N.Y.)},
+  shortjournal  = {Science},
+  year          = {1997},
+}
+
+@Book{berlinet2011reproducing,
+  author        = {Berlinet, Alain and Thomas-Agnan, Christine},
+  title         = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics},
+  publisher     = {{Springer Science \& Business Media}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2011},
+}
+
+@Misc{bernanke1990federal,
+  author        = {Bernanke, Ben S},
+  title         = {The Federal Funds Rate and the Channels of Monetary Transnission},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  publisher     = {{National Bureau of Economic Research Cambridge, Mass., USA}},
+  year          = {1990},
+}
+
+@Article{besbes2014stochastic,
+  author        = {Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
+  title         = {Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards},
+  pages         = {199--207},
+  volume        = {27},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in neural information processing systems},
+  year          = {2014},
+}
+
+@Article{bholat2020impact,
+  author        = {Bholat, D and Gharbawi, M and Thew, O},
+  title         = {The {{Impact}} of {{Covid}} on {{Machine Learning}} and {{Data Science}} in {{UK Banking}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Bank of England Quarterly Bulletin, Q4},
+  year          = {2020},
+}
+
+@Book{bishop2006pattern,
+  author        = {Bishop, Christopher M},
+  title         = {Pattern Recognition and Machine Learning},
+  publisher     = {{springer}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2006},
+}
+
+@Article{blaom2020mlj,
+  author        = {Blaom, Anthony D. and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J.},
+  title         = {{{MLJ}}: {{A Julia}} Package for Composable Machine Learning},
+  doi           = {10.21105/joss.02704},
+  issn          = {2475-9066},
+  number        = {55},
+  pages         = {2704},
+  urldate       = {2022-10-27},
+  volume        = {5},
+  abstract      = {Blaom et al., (2020). MLJ: A Julia package for composable machine learning. Journal of Open Source Software, 5(55), 2704, https://doi.org/10.21105/joss.02704},
+  bdsk-url-1    = {https://joss.theoj.org/papers/10.21105/joss.02704},
+  bdsk-url-2    = {https://doi.org/10.21105/joss.02704},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  file          = {:/Users/FA31DU/Zotero/storage/7AY87FGP/Blaom et al. - 2020 - MLJ A Julia package for composable machine learni.pdf:;:/Users/FA31DU/Zotero/storage/D69YSMVF/joss.html:},
+  journal       = {Journal of Open Source Software},
+  langid        = {english},
+  month         = nov,
+  shorttitle    = {{{MLJ}}},
+  year          = {2020},
+}
+
+@InProceedings{blundell2015weight,
+  author        = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
+  booktitle     = {International Conference on Machine Learning},
+  title         = {Weight Uncertainty in Neural Network},
+  pages         = {1613--1622},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2015},
+}
+
+@Article{borch2022machine,
+  author        = {Borch, Christian},
+  title         = {Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading},
+  pages         = {101852},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Technology in Society},
+  year          = {2022},
+}
+
+@Unpublished{borisov2021deep,
+  author        = {Borisov, Vadim and Leemann, Tobias and Se{\ss}ler, Kathrin and Haug, Johannes and Pawelczyk, Martin and Kasneci, Gjergji},
+  title         = {Deep Neural Networks and Tabular Data: {{A}} Survey},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2110.01889},
+  eprinttype    = {arxiv},
+  year          = {2021},
+}
+
+@Article{bramoulle2009identification,
+  author        = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+  title         = {Identification of Peer Effects through Social Networks},
+  number        = {1},
+  pages         = {41--55},
+  volume        = {150},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of econometrics},
+  year          = {2009},
+}
+
+@Article{bramoulle2020peer,
+  author        = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+  title         = {Peer Effects in Networks: {{A}} Survey},
+  pages         = {603--629},
+  volume        = {12},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Annual Review of Economics},
+  year          = {2020},
+}
+
+@Unpublished{branco2015survey,
+  author        = {Branco, Paula and Torgo, Luis and Ribeiro, Rita},
+  title         = {A Survey of Predictive Modelling under Imbalanced Distributions},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1505.01658},
+  eprinttype    = {arxiv},
+  year          = {2015},
+}
+
+@Book{brock1991nonlinear,
+  author        = {Brock, William Allen and Brock, William A and Hsieh, David Arthur and LeBaron, Blake Dean and Brock, William E},
+  title         = {Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence},
+  publisher     = {{MIT press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1991},
+}
+
+@InProceedings{buolamwini2018gender,
+  author        = {Buolamwini, Joy and Gebru, Timnit},
+  booktitle     = {Conference on Fairness, Accountability and Transparency},
+  title         = {Gender Shades: {{Intersectional}} Accuracy Disparities in Commercial Gender Classification},
+  pages         = {77--91},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2018},
+}
+
+@Unpublished{bussmann2020neural,
+  author        = {Bussmann, Bart and Nys, Jannes and Latr{\'e}, Steven},
+  title         = {Neural {{Additive Vector Autoregression Models}} for {{Causal Discovery}} in {{Time Series Data}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2010.09429},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Report{card1993minimum,
+  author        = {Card, David and Krueger, Alan B},
+  title         = {Minimum Wages and Employment: {{A}} Case Study of the Fast Food Industry in {{New Jersey}} and {{Pennsylvania}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  school        = {{National Bureau of Economic Research}},
+  year          = {1993},
+}
+
+@InProceedings{carlini2017evaluating,
+  author        = {Carlini, Nicholas and Wagner, David},
+  booktitle     = {2017 Ieee Symposium on Security and Privacy (Sp)},
+  title         = {Towards Evaluating the Robustness of Neural Networks},
+  pages         = {39--57},
+  publisher     = {{IEEE}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2017},
+}
+
+@Article{carlisle2019racist,
+  author        = {Carlisle, M.},
+  title         = {Racist Data Destruction? - a {{Boston}} Housing Dataset Controversy},
+  url           = {https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8},
+  bdsk-url-1    = {https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2019},
+}
+
+@Article{carrell2009does,
+  author        = {Carrell, Scott E and Fullerton, Richard L and West, James E},
+  title         = {Does Your Cohort Matter? {{Measuring}} Peer Effects in College Achievement},
+  number        = {3},
+  pages         = {439--464},
+  volume        = {27},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Labor Economics},
+  year          = {2009},
+}
+
+@Article{carrell2013natural,
+  author        = {Carrell, Scott E and Sacerdote, Bruce I and West, James E},
+  title         = {From Natural Variation to Optimal Policy? {{The}} Importance of Endogenous Peer Group Formation},
+  number        = {3},
+  pages         = {855--882},
+  volume        = {81},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica : journal of the Econometric Society},
+  shortjournal  = {Econometrica},
+  year          = {2013},
+}
+
+@Article{carrizosa2021generating,
+  author        = {Carrizosa, Emilio and Ramırez-Ayerbe, Jasone and Romero, Dolores},
+  title         = {Generating {{Collective Counterfactual Explanations}} in {{Score-Based Classification}} via {{Mathematical Optimization}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Article{cascarino2022explainable,
+  author        = {Cascarino, Giuseppe and Moscatelli, Mirko and Parlapiano, Fabio},
+  title         = {Explainable {{Artificial Intelligence}}: Interpreting Default Forecasting Models Based on {{Machine Learning}}},
+  number        = {674},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Bank of Italy Occasional Paper},
+  year          = {2022},
+}
+
+@Article{chandola2009anomaly,
+  author        = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
+  title         = {Anomaly Detection: {{A}} Survey},
+  number        = {3},
+  pages         = {1--58},
+  volume        = {41},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {ACM computing surveys (CSUR)},
+  year          = {2009},
+}
+
+@Article{chapelle2011empirical,
+  author        = {Chapelle, Olivier and Li, Lihong},
+  title         = {An Empirical Evaluation of Thompson Sampling},
+  pages         = {2249--2257},
+  volume        = {24},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in neural information processing systems},
+  year          = {2011},
+}
+
+@Article{chetty2011adjustment,
+  author        = {Chetty, Raj and Friedman, John N and Olsen, Tore and Pistaferri, Luigi},
+  title         = {Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: {{Evidence}} from {{Danish}} Tax Records},
+  number        = {2},
+  pages         = {749--804},
+  volume        = {126},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The quarterly journal of economics},
+  year          = {2011},
+}
+
+@Article{cortes1995supportvector,
+  author        = {Cortes, Corinna and Vapnik, Vladimir},
+  title         = {Support-Vector Networks},
+  number        = {3},
+  pages         = {273--297},
+  volume        = {20},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Machine learning},
+  year          = {1995},
+}
+
+@Article{crawford2019variable,
+  author        = {Crawford, Lorin and Flaxman, Seth R and Runcie, Daniel E and West, Mike},
+  title         = {Variable Prioritization in Nonlinear Black Box Methods: {{A}} Genetic Association Case Study},
+  number        = {2},
+  pages         = {958},
+  volume        = {13},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The annals of applied statistics},
+  year          = {2019},
+}
+
+@InProceedings{dai2022counterfactual,
+  author        = {Dai, Xinyue and Keane, Mark T and Shalloo, Laurence and Ruelle, Elodie and Byrne, Ruth MJ},
+  title         = {Counterfactual Explanations for Prediction and Diagnosis in Xai},
+  eventtitle    = {Proceedings of the 2022 {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+  pages         = {215--226},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2022},
+}
+
+@Article{danielsson2021artificial,
+  author        = {Danielsson, Jon and Macrae, Robert and Uthemann, Andreas},
+  title         = {Artificial Intelligence and Systemic Risk},
+  pages         = {106290},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Banking \& Finance},
+  year          = {2021},
+}
+
+@Article{daxberger2021laplace,
+  author        = {Daxberger, Erik and Kristiadi, Agustinus and Immer, Alexander and Eschenhagen, Runa and Bauer, Matthias and Hennig, Philipp},
+  title         = {Laplace {{Redux-Effortless Bayesian Deep Learning}}},
+  volume        = {34},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in Neural Information Processing Systems},
+  year          = {2021},
+}
+
+@Article{dehejia1999causal,
+  author        = {Dehejia, Rajeev H and Wahba, Sadek},
+  title         = {Causal Effects in Nonexperimental Studies: {{Reevaluating}} the Evaluation of Training Programs},
+  number        = {448},
+  pages         = {1053--1062},
+  volume        = {94},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of the American statistical Association},
+  year          = {1999},
+}
+
+@Article{dell2010persistent,
+  author        = {Dell, Melissa},
+  title         = {The Persistent Effects of {{Peru}}'s Mining Mita},
+  number        = {6},
+  pages         = {1863--1903},
+  volume        = {78},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica : journal of the Econometric Society},
+  shortjournal  = {Econometrica},
+  year          = {2010},
+}
+
+@Article{denhengst2020reinforcement,
+  author        = {den Hengst, Floris and Grua, Eoin Martino and el Hassouni, Ali and Hoogendoorn, Mark},
+  title         = {Reinforcement Learning for Personalization: {{A}} Systematic Literature Review},
+  issue         = {Preprint},
+  pages         = {1--41},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Data Science},
+  options       = {useprefix=true},
+  year          = {2020},
+}
+
+@Article{deoliveira2021framework,
+  author        = {de Oliveira, Raphael Mazzine Barbosa and Martens, David},
+  title         = {A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data},
+  number        = {16},
+  pages         = {7274},
+  volume        = {11},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Applied Sciences},
+  options       = {useprefix=true},
+  year          = {2021},
+}
+
+@Article{dhurandhar2018explanations,
+  author        = {Dhurandhar, Amit and Chen, Pin-Yu and Luss, Ronny and Tu, Chun-Chen and Ting, Paishun and Shanmugam, Karthikeyan and Das, Payel},
+  title         = {Explanations Based on the Missing: {{Towards}} Contrastive Explanations with Pertinent Negatives},
+  volume        = {31},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in neural information processing systems},
+  year          = {2018},
+}
+
+@InProceedings{dombrowski2021diffeomorphic,
+  author        = {Dombrowski, Ann-Kathrin and Gerken, Jan E and Kessel, Pan},
+  booktitle     = {{{ICML Workshop}} on {{Invertible Neural Networks}}, {{Normalizing Flows}}, and {{Explicit Likelihood Models}}},
+  title         = {Diffeomorphic Explanations with Normalizing Flows},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@InProceedings{dorffner1996neural,
+  author        = {Dorffner, Georg},
+  booktitle     = {Neural Network World},
+  title         = {Neural Networks for Time Series Processing},
+  publisher     = {{Citeseer}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1996},
+}
+
+@Article{epstein1979stability,
+  author        = {Epstein, Seymour},
+  title         = {The Stability of Behavior: {{I}}. {{On}} Predicting Most of the People Much of the Time.},
+  number        = {7},
+  pages         = {1097},
+  volume        = {37},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of personality and social psychology},
+  year          = {1979},
+}
+
+@Online{barocas2022fairness,
+  author        = {Solon Barocas and Moritz Hardt and Arvind Narayanan},
+  title         = {Fairness and Machine Learning},
+  url           = {https://fairmlbook.org/index.html},
+  urldate       = {2022-11-08},
+  bdsk-url-1    = {https://fairmlbook.org/index.html},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  month         = dec,
+  year          = {2022},
+}
+
+@Article{falk2006clean,
+  author        = {Falk, Armin and Ichino, Andrea},
+  title         = {Clean Evidence on Peer Effects},
+  number        = {1},
+  pages         = {39--57},
+  volume        = {24},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of labor economics},
+  year          = {2006},
+}
+
+@Unpublished{fan2020interpretability,
+  author        = {Fan, Fenglei and Xiong, Jinjun and Wang, Ge},
+  title         = {On Interpretability of Artificial Neural Networks},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2001.02522},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Article{fang2011dynamic,
+  author        = {Fang, Hanming and Gavazza, Alessandro},
+  title         = {Dynamic Inefficiencies in an Employment-Based Health Insurance System: {{Theory}} and Evidence},
+  number        = {7},
+  pages         = {3047--77},
+  volume        = {101},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {American Economic Review},
+  year          = {2011},
+}
+
+@Article{fehr2000cooperation,
+  author        = {Fehr, Ernst and Gachter, Simon},
+  title         = {Cooperation and Punishment in Public Goods Experiments},
+  number        = {4},
+  pages         = {980--994},
+  volume        = {90},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {American Economic Review},
+  year          = {2000},
+}
+
+@Article{fix1951important,
+  author        = {Fix, E and Hodges, J},
+  title         = {An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation},
+  number        = {57},
+  pages         = {233--238},
+  volume        = {3},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {International Statistical Review},
+  year          = {1951},
+}
+
+@Book{friedman2008monetary,
+  author        = {Friedman, Milton and Schwartz, Anna Jacobson},
+  title         = {A Monetary History of the {{United States}}, 1867-1960},
+  publisher     = {{Princeton University Press}},
+  volume        = {14},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2008},
+}
+
+@InProceedings{gal2016dropout,
+  author        = {Gal, Yarin and Ghahramani, Zoubin},
+  booktitle     = {International Conference on Machine Learning},
+  title         = {Dropout as a Bayesian Approximation: {{Representing}} Model Uncertainty in Deep Learning},
+  pages         = {1050--1059},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2016},
+}
+
+@InProceedings{gal2017deep,
+  author        = {Gal, Yarin and Islam, Riashat and Ghahramani, Zoubin},
+  booktitle     = {International {{Conference}} on {{Machine Learning}}},
+  title         = {Deep Bayesian Active Learning with Image Data},
+  pages         = {1183--1192},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2017},
+}
+
+@Article{galizzi2019external,
+  author        = {Galizzi, Matteo M and Navarro-Martinez, Daniel},
+  title         = {On the External Validity of Social Preference Games: A Systematic Lab-Field Study},
+  number        = {3},
+  pages         = {976--1002},
+  volume        = {65},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Management Science},
+  year          = {2019},
+}
+
+@Article{gama2014survey,
+  author        = {Gama, Jo{\~a}o and {\v Z}liobait{\.e}, Indr{\.e} and Bifet, Albert and Pechenizkiy, Mykola and Bouchachia, Abdelhamid},
+  title         = {A Survey on Concept Drift Adaptation},
+  number        = {4},
+  pages         = {1--37},
+  volume        = {46},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {ACM computing surveys (CSUR)},
+  year          = {2014},
+}
+
+@Unpublished{garivier2008upperconfidence,
+  author        = {Garivier, Aur{\'e}lien and Moulines, Eric},
+  title         = {On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {0805.3415},
+  eprinttype    = {arxiv},
+  year          = {2008},
+}
+
+@Book{gelman2013bayesian,
+  author        = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
+  title         = {Bayesian Data Analysis},
+  publisher     = {{CRC press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2013},
+}
+
+@Article{gilbert1998immune,
+  author        = {Gilbert, Daniel T and Pinel, Elizabeth C and Wilson, Timothy D and Blumberg, Stephen J and Wheatley, Thalia P},
+  title         = {Immune Neglect: A Source of Durability Bias in Affective Forecasting.},
+  number        = {3},
+  pages         = {617},
+  volume        = {75},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of personality and social psychology},
+  year          = {1998},
+}
+
+@Article{gneezy2006uncertainty,
+  author        = {Gneezy, Uri and List, John A and Wu, George},
+  title         = {The Uncertainty Effect: {{When}} a Risky Prospect Is Valued Less than Its Worst Possible Outcome},
+  number        = {4},
+  pages         = {1283--1309},
+  volume        = {121},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Quarterly Journal of Economics},
+  year          = {2006},
+}
+
+@InCollection{goan2020bayesian,
+  author        = {Goan, Ethan and Fookes, Clinton},
+  booktitle     = {Case {{Studies}} in {{Applied Bayesian Data Science}}},
+  title         = {Bayesian {{Neural Networks}}: {{An Introduction}} and {{Survey}}},
+  pages         = {45--87},
+  publisher     = {{Springer}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Article{goldsmith-pinkham2013social,
+  author        = {Goldsmith-Pinkham, Paul and Imbens, Guido W},
+  title         = {Social Networks and the Identification of Peer Effects},
+  number        = {3},
+  pages         = {253--264},
+  volume        = {31},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Business \& Economic Statistics},
+  year          = {2013},
+}
+
+@Unpublished{goodfellow2014explaining,
+  author        = {Goodfellow, Ian J and Shlens, Jonathon and Szegedy, Christian},
+  title         = {Explaining and Harnessing Adversarial Examples},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1412.6572},
+  eprinttype    = {arxiv},
+  year          = {2014},
+}
+
+@Book{goodfellow2016deep,
+  author        = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
+  title         = {Deep {{Learning}}},
+  publisher     = {{MIT Press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2016},
+}
+
+@Article{goodfriend2005incredible,
+  author        = {Goodfriend, Marvin and King, Robert G},
+  title         = {The Incredible {{Volcker}} Disinflation},
+  number        = {5},
+  pages         = {981--1015},
+  volume        = {52},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Monetary Economics},
+  year          = {2005},
+}
+
+@Article{graham2017econometric,
+  author        = {Graham, Bryan S},
+  title         = {An Econometric Model of Network Formation with Degree Heterogeneity},
+  number        = {4},
+  pages         = {1033--1063},
+  volume        = {85},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica : journal of the Econometric Society},
+  shortjournal  = {Econometrica},
+  year          = {2017},
+}
+
+@Article{greene2012econometric,
+  author        = {Greene, William H},
+  title         = {Econometric Analysis, 71e},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Stern School of Business, New York University},
+  year          = {2012},
+}
+
+@Article{grether1979economic,
+  author        = {Grether, David M and Plott, Charles R},
+  title         = {Economic Theory of Choice and the Preference Reversal Phenomenon},
+  number        = {4},
+  pages         = {623--638},
+  volume        = {69},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The American Economic Review},
+  year          = {1979},
+}
+
+@Article{gretton2012kernel,
+  author        = {Gretton, Arthur and Borgwardt, Karsten M and Rasch, Malte J and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
+  title         = {A Kernel Two-Sample Test},
+  number        = {1},
+  pages         = {723--773},
+  volume        = {13},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Journal of Machine Learning Research},
+  year          = {2012},
+}
+
+@Unpublished{griffith2020name,
+  author        = {Griffith, Alan},
+  title         = {Name {{Your Friends}}, but {{Only Five}}? {{The Importance}} of {{Censoring}} in {{Peer Effects Estimates}} Using {{Social Network Data}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Unpublished{grinsztajn2022why,
+  author        = {Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l},
+  title         = {Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2207.08815},
+  eprinttype    = {arxiv},
+  year          = {2022},
+}
+
+@Misc{group2020detailed,
+  author        = {Group, Open COVID-19 Data Working},
+  title         = {Detailed {{Epidemiological Data}} from the {{COVID-19 Outbreak}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@InProceedings{gupta2011thompson,
+  author        = {Gupta, Neha and Granmo, Ole-Christoffer and Agrawala, Ashok},
+  booktitle     = {2011 10th {{International Conference}} on {{Machine Learning}} and {{Applications}} and {{Workshops}}},
+  title         = {Thompson Sampling for Dynamic Multi-Armed Bandits},
+  pages         = {484--489},
+  publisher     = {{IEEE}},
+  volume        = {1},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2011},
+}
+
+@Book{hamilton2020time,
+  author        = {Hamilton, James Douglas},
+  title         = {Time Series Analysis},
+  publisher     = {{Princeton university press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Article{hamon2020robustness,
+  author        = {Hamon, Ronan and Junklewitz, Henrik and Sanchez, Ignacio},
+  title         = {Robustness and Explainability of Artificial Intelligence},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Publications Office of the European Union},
+  year          = {2020},
+}
+
+@Article{hamzacebi2008improving,
+  author        = {Hamza{\c c}ebi, Co{\c s}kun},
+  title         = {Improving Artificial Neural Networks' Performance in Seasonal Time Series Forecasting},
+  number        = {23},
+  pages         = {4550--4559},
+  volume        = {178},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Information Sciences},
+  year          = {2008},
+}
+
+@InProceedings{hanneke2007bound,
+  author        = {Hanneke, Steve},
+  booktitle     = {Proceedings of the 24th International Conference on {{Machine}} Learning},
+  title         = {A Bound on the Label Complexity of Agnostic Active Learning},
+  pages         = {353--360},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2007},
+}
+
+@Article{hansen2020virtue,
+  author        = {Hansen, Kristian Bondo},
+  title         = {The Virtue of Simplicity: {{On}} Machine Learning Models in Algorithmic Trading},
+  number        = {1},
+  pages         = {2053951720926558},
+  volume        = {7},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Big Data \& Society},
+  year          = {2020},
+}
+
+@Article{hartland2006multiarmed,
+  author        = {Hartland, C{\'e}dric and Gelly, Sylvain and Baskiotis, Nicolas and Teytaud, Olivier and Sebag, Michele},
+  title         = {Multi-Armed Bandit, Dynamic Environments and Meta-Bandits},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2006},
+}
+
+@Article{heckman1985alternative,
+  author        = {Heckman, James J and Robb Jr, Richard},
+  title         = {Alternative Methods for Evaluating the Impact of Interventions: {{An}} Overview},
+  number        = {1-2},
+  pages         = {239--267},
+  volume        = {30},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of econometrics},
+  year          = {1985},
+}
+
+@Article{hershfield2011increasing,
+  author        = {Hershfield, Hal E and Goldstein, Daniel G and Sharpe, William F and Fox, Jesse and Yeykelis, Leo and Carstensen, Laura L and Bailenson, Jeremy N},
+  title         = {Increasing Saving Behavior through Age-Progressed Renderings of the Future Self},
+  issue         = {SPL},
+  pages         = {S23--S37},
+  volume        = {48},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Marketing Research},
+  year          = {2011},
+}
+
+@InProceedings{ho1995random,
+  author        = {Ho, Tin Kam},
+  booktitle     = {Proceedings of 3rd International Conference on Document Analysis and Recognition},
+  title         = {Random Decision Forests},
+  pages         = {278--282},
+  publisher     = {{IEEE}},
+  volume        = {1},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1995},
+}
+
+@Article{hochreiter1997long,
+  author        = {Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
+  title         = {Long Short-Term Memory},
+  number        = {8},
+  pages         = {1735--1780},
+  volume        = {9},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Neural computation},
+  year          = {1997},
+}
+
+@Unpublished{hoff2021bayesoptimal,
+  author        = {Hoff, Peter},
+  title         = {Bayes-Optimal Prediction with Frequentist Coverage Control},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2105.14045},
+  eprinttype    = {arxiv},
+  file          = {:/Users/FA31DU/Zotero/storage/IQK27WVA/Hoff - 2021 - Bayes-optimal prediction with frequentist coverage.pdf:;:/Users/FA31DU/Zotero/storage/K8EAZA25/2105.html:},
+  year          = {2021},
+}
+
+@Misc{hoffman1994german,
+  author        = {Hoffman, Hans},
+  title         = {German {{Credit Data}}},
+  url           = {https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)},
+  bdsk-url-1    = {https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1994},
+}
+
+@Online{hoffmanGermanCreditData1994,
+  author        = {Hoffman, Hans},
+  title         = {German {{Credit Data}}},
+  url           = {https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)},
+  bdsk-url-1    = {https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1994},
+}
+
+@Unpublished{houlsby2011bayesian,
+  author        = {Houlsby, Neil and Husz{\'a}r, Ferenc and Ghahramani, Zoubin and Lengyel, M{\'a}t{\'e}},
+  title         = {Bayesian Active Learning for Classification and Preference Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1112.5745},
+  eprinttype    = {arxiv},
+  year          = {2011},
+}
+
+@Article{hsee1996evaluability,
+  author        = {Hsee, Christopher K},
+  title         = {The Evaluability Hypothesis: {{An}} Explanation for Preference Reversals between Joint and Separate Evaluations of Alternatives},
+  number        = {3},
+  pages         = {247--257},
+  volume        = {67},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Organizational behavior and human decision processes},
+  year          = {1996},
+}
+
+@Article{hsee2004music,
+  author        = {Hsee, Christopher K and Rottenstreich, Yuval},
+  title         = {Music, Pandas, and Muggers: On the Affective Psychology of Value.},
+  number        = {1},
+  pages         = {23},
+  volume        = {133},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Experimental Psychology: General},
+  year          = {2004},
+}
+
+@Article{hsieh2016social,
+  author        = {Hsieh, Chih-Sheng and Lee, Lung Fei},
+  title         = {A Social Interactions Model with Endogenous Friendship Formation and Selectivity},
+  number        = {2},
+  pages         = {301--319},
+  volume        = {31},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Applied Econometrics},
+  year          = {2016},
+}
+
+@Unpublished{immer2020improving,
+  author        = {Immer, Alexander and Korzepa, Maciej and Bauer, Matthias},
+  title         = {Improving Predictions of Bayesian Neural Networks via Local Linearization},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2008.08400},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Unpublished{innes2018fashionable,
+  author        = {Innes, Michael and Saba, Elliot and Fischer, Keno and Gandhi, Dhairya and Rudilosso, Marco Concetto and Joy, Neethu Mariya and Karmali, Tejan and Pal, Avik and Shah, Viral},
+  title         = {Fashionable Modelling with Flux},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1811.01457},
+  eprinttype    = {arxiv},
+  year          = {2018},
+}
+
+@Article{innes2018flux,
+  author        = {Innes, Mike},
+  title         = {Flux: {{Elegant}} Machine Learning with {{Julia}}},
+  number        = {25},
+  pages         = {602},
+  volume        = {3},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Open Source Software},
+  year          = {2018},
+}
+
+@Unpublished{ish-horowicz2019interpreting,
+  author        = {Ish-Horowicz, Jonathan and Udwin, Dana and Flaxman, Seth and Filippi, Sarah and Crawford, Lorin},
+  title         = {Interpreting Deep Neural Networks through Variable Importance},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1901.09839},
+  eprinttype    = {arxiv},
+  year          = {2019},
+}
+
+@InProceedings{jabbari2017fairness,
+  author        = {Jabbari, Shahin and Joseph, Matthew and Kearns, Michael and Morgenstern, Jamie and Roth, Aaron},
+  booktitle     = {International {{Conference}} on {{Machine Learning}}},
+  title         = {Fairness in Reinforcement Learning},
+  pages         = {1617--1626},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2017},
+}
+
+@Article{jackson2007meeting,
+  author        = {Jackson, Matthew O and Rogers, Brian W},
+  title         = {Meeting Strangers and Friends of Friends: {{How}} Random Are Social Networks?},
+  number        = {3},
+  pages         = {890--915},
+  volume        = {97},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {American Economic Review},
+  year          = {2007},
+}
+
+@Unpublished{jeanneret2022diffusion,
+  author        = {Jeanneret, Guillaume and Simon, Lo{\"\i}c and Jurie, Fr{\'e}d{\'e}ric},
+  title         = {Diffusion {{Models}} for {{Counterfactual Explanations}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2203.15636},
+  eprinttype    = {arxiv},
+  year          = {2022},
+}
+
+@Article{johansson2005failure,
+  author        = {Johansson, Petter and Hall, Lars and Sikstr{\"o}m, Sverker and Olsson, Andreas},
+  title         = {Failure to Detect Mismatches between Intention and Outcome in a Simple Decision Task},
+  number        = {5745},
+  pages         = {116--119},
+  volume        = {310},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Science (New York, N.Y.)},
+  shortjournal  = {Science},
+  year          = {2005},
+}
+
+@Article{johnsson2021estimation,
+  author        = {Johnsson, Ida and Moon, Hyungsik Roger},
+  title         = {Estimation of Peer Effects in Endogenous Social Networks: {{Control}} Function Approach},
+  number        = {2},
+  pages         = {328--345},
+  volume        = {103},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Review of Economics and Statistics},
+  year          = {2021},
+}
+
+@Article{jolliffe2003modified,
+  author        = {Jolliffe, Ian T and Trendafilov, Nickolay T and Uddin, Mudassir},
+  title         = {A Modified Principal Component Technique Based on the {{LASSO}}},
+  number        = {3},
+  pages         = {531--547},
+  volume        = {12},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of computational and Graphical Statistics},
+  year          = {2003},
+}
+
+@Article{joseph2021forecasting,
+  author        = {Joseph, Andreas and Kalamara, Eleni and Kapetanios, George and Potjagailo, Galina},
+  title         = {Forecasting Uk Inflation Bottom Up},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Unpublished{joshi2019realistic,
+  author        = {Joshi, Shalmali and Koyejo, Oluwasanmi and Vijitbenjaronk, Warut and Kim, Been and Ghosh, Joydeep},
+  title         = {Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1907.09615},
+  eprinttype    = {arxiv},
+  year          = {2019},
+}
+
+@Unpublished{jospin2020handson,
+  author        = {Jospin, Laurent Valentin and Buntine, Wray and Boussaid, Farid and Laga, Hamid and Bennamoun, Mohammed},
+  title         = {Hands-on {{Bayesian Neural Networks}}--a {{Tutorial}} for {{Deep Learning Users}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2007.06823},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Misc{kaggle2011give,
+  author        = {Kaggle},
+  title         = {Give Me Some Credit, {{Improve}} on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the next Two Years.},
+  url           = {https://www.kaggle.com/c/GiveMeSomeCredit},
+  bdsk-url-1    = {https://www.kaggle.com/c/GiveMeSomeCredit},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  publisher     = {{Kaggle}},
+  year          = {2011},
+}
+
+@online{kagglecompetitionGiveMeCredit,
+	author = {Kaggle Competition},
+	date-added = {2022-12-13 12:58:01 +0100},
+	date-modified = {2022-12-13 12:58:01 +0100},
+	title = {Give Me Some Credit, {{Improve}} on the State of the Art in Credit Scoring by Predicting the Probability That Somebody Will Experience Financial Distress in the next Two Years.},
+	url = {https://www.kaggle.com/c/GiveMeSomeCredit},
+	bdsk-url-1 = {https://www.kaggle.com/c/GiveMeSomeCredit}}
+
+@Article{kahneman1979prospect,
+  author        = {Kahneman, Daniel and Tversky, Amos},
+  title         = {Prospect {{Theory}}: {{An Analysis}} of {{Decision}} under {{Risk}}},
+  pages         = {263--291},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica: Journal of the Econometric Society},
+  year          = {1979},
+}
+
+@Article{kahneman1990experimental,
+  author        = {Kahneman, Daniel and Knetsch, Jack L and Thaler, Richard H},
+  title         = {Experimental Tests of the Endowment Effect and the {{Coase}} Theorem},
+  number        = {6},
+  pages         = {1325--1348},
+  volume        = {98},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of political Economy},
+  year          = {1990},
+}
+
+@Article{kahneman1992reference,
+  author        = {Kahneman, Daniel},
+  title         = {Reference Points, Anchors, Norms, and Mixed Feelings},
+  number        = {2},
+  pages         = {296--312},
+  volume        = {51},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Organizational behavior and human decision processes},
+  year          = {1992},
+}
+
+@Unpublished{karimi2020algorithmic,
+  author        = {Karimi, Amir-Hossein and Von K{\"u}gelgen, Julius and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+  title         = {Algorithmic Recourse under Imperfect Causal Knowledge: A Probabilistic Approach},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2006.06831},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Unpublished{karimi2020survey,
+  author        = {Karimi, Amir-Hossein and Barthe, Gilles and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+  title         = {A Survey of Algorithmic Recourse: Definitions, Formulations, Solutions, and Prospects},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2010.04050},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@InProceedings{karimi2021algorithmic,
+  author        = {Karimi, Amir-Hossein and Sch{\"o}lkopf, Bernhard and Valera, Isabel},
+  booktitle     = {Proceedings of the 2021 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  title         = {Algorithmic Recourse: From Counterfactual Explanations to Interventions},
+  pages         = {353--362},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@InProceedings{kaur2020interpreting,
+  author        = {Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer},
+  booktitle     = {Proceedings of the 2020 {{CHI}} Conference on Human Factors in Computing Systems},
+  title         = {Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning},
+  pages         = {1--14},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Article{kehoe2021defence,
+  author        = {Kehoe, Aidan and Wittek, Peter and Xue, Yanbo and Pozas-Kerstjens, Alejandro},
+  title         = {Defence against Adversarial Attacks Using Classical and Quantum-Enhanced {{Boltzmann}} Machines},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Machine Learning: Science and Technology},
+  year          = {2021},
+}
+
+@Unpublished{kendall2017what,
+  author        = {Kendall, Alex and Gal, Yarin},
+  title         = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1703.04977},
+  eprinttype    = {arxiv},
+  year          = {2017},
+}
+
+@Article{kihoro2004seasonal,
+  author        = {Kihoro, J and Otieno, RO and Wafula, C},
+  title         = {Seasonal Time Series Forecasting: {{A}} Comparative Study of {{ARIMA}} and {{ANN}} Models},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2004},
+}
+
+@Book{kilian2017structural,
+  author        = {Kilian, Lutz and L{\"u}tkepohl, Helmut},
+  title         = {Structural Vector Autoregressive Analysis},
+  publisher     = {{Cambridge University Press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2017},
+}
+
+@Unpublished{kingma2014adam,
+  author        = {Kingma, Diederik P and Ba, Jimmy},
+  title         = {Adam: {{A}} Method for Stochastic Optimization},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1412.6980},
+  eprinttype    = {arxiv},
+  year          = {2014},
+}
+
+@Article{kirsch2019batchbald,
+  author        = {Kirsch, Andreas and Van Amersfoort, Joost and Gal, Yarin},
+  title         = {Batchbald: {{Efficient}} and Diverse Batch Acquisition for Deep Bayesian Active Learning},
+  pages         = {7026--7037},
+  volume        = {32},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in neural information processing systems},
+  year          = {2019},
+}
+
+@Unpublished{kuiper2021exploring,
+  author        = {Kuiper, Ouren and van den Berg, Martin and van den Burgt, Joost and Leijnen, Stefan},
+  title         = {Exploring {{Explainable AI}} in the {{Financial Sector}}: {{Perspectives}} of {{Banks}} and {{Supervisory Authorities}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2111.02244},
+  eprinttype    = {arxiv},
+  year          = {2021},
+}
+
+@Article{kydland1982time,
+  author        = {Kydland, Finn E and Prescott, Edward C},
+  title         = {Time to Build and Aggregate Fluctuations},
+  pages         = {1345--1370},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica: Journal of the Econometric Society},
+  year          = {1982},
+}
+
+@Unpublished{lachapelle2019gradientbased,
+  author        = {Lachapelle, S{\'e}bastien and Brouillard, Philippe and Deleu, Tristan and Lacoste-Julien, Simon},
+  title         = {Gradient-Based Neural Dag Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1906.02226},
+  eprinttype    = {arxiv},
+  year          = {2019},
+}
+
+@InProceedings{lakkaraju2020how,
+  author        = {Lakkaraju, Himabindu and Bastani, Osbert},
+  booktitle     = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+  title         = {" {{How}} Do {{I}} Fool You?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}},
+  pages         = {79--85},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@InProceedings{lakkaraju2020how,
+  author        = {Lakkaraju, Himabindu and Bastani, Osbert},
+  booktitle     = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+  title         = {" {{How Do I Fool You}}?" {{Manipulating User Trust}} via {{Misleading Black Box Explanations}}},
+  pages         = {79--85},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Unpublished{lakshminarayanan2016simple,
+  author        = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
+  title         = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1612.01474},
+  eprinttype    = {arxiv},
+  year          = {2016},
+}
+
+@Unpublished{laugel2017inverse,
+  author        = {Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
+  title         = {Inverse Classification for Comparison-Based Interpretability in Machine Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1712.08443},
+  eprinttype    = {arxiv},
+  shortjournal  = {arXiv preprint arXiv:1712.08443},
+  year          = {2017},
+}
+
+@Thesis{lawrence2001variational,
+  author        = {Lawrence, Neil David},
+  title         = {Variational Inference in Probabilistic Models},
+  type          = {phdthesis},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  school        = {{University of Cambridge}},
+  year          = {2001},
+}
+
+@Article{lecun1998mnist,
+  author        = {LeCun, Yann},
+  title         = {The {{MNIST}} Database of Handwritten Digits},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  shortjournal  = {http://yann. lecun. com/exdb/mnist/},
+  year          = {1998},
+}
+
+@Article{lee2003best,
+  author        = {Lee, Lung-fei},
+  title         = {Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances},
+  number        = {4},
+  pages         = {307--335},
+  volume        = {22},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometric Reviews},
+  year          = {2003},
+}
+
+@Article{lerner2013financial,
+  author        = {Lerner, Jennifer S and Li, Ye and Weber, Elke U},
+  title         = {The Financial Costs of Sadness},
+  number        = {1},
+  pages         = {72--79},
+  volume        = {24},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Psychological science},
+  year          = {2013},
+}
+
+@Article{list2004neoclassical,
+  author        = {List, John A},
+  title         = {Neoclassical Theory versus Prospect Theory: {{Evidence}} from the Marketplace},
+  number        = {2},
+  pages         = {615--625},
+  volume        = {72},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Econometrica : journal of the Econometric Society},
+  shortjournal  = {Econometrica},
+  year          = {2004},
+}
+
+@Article{lucas1976econometric,
+  author        = {Lucas, JR},
+  title         = {Econometric Policy Evaluation: A Critique `, in {{K}}. {{Brunner}} and {{A Meltzer}}, {{The Phillips}} Curve and Labor Markets, {{North Holland}}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {1976},
+}
+
+@InProceedings{lundberg2017unified,
+  author        = {Lundberg, Scott M and Lee, Su-In},
+  booktitle     = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
+  title         = {A Unified Approach to Interpreting Model Predictions},
+  pages         = {4768--4777},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2017},
+}
+
+@Book{lutkepohl2005new,
+  author        = {L{\"u}tkepohl, Helmut},
+  title         = {New Introduction to Multiple Time Series Analysis},
+  publisher     = {{Springer Science \& Business Media}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2005},
+}
+
+@Article{madrian2001power,
+  author        = {Madrian, Brigitte C and Shea, Dennis F},
+  title         = {The Power of Suggestion: {{Inertia}} in 401 (k) Participation and Savings Behavior},
+  number        = {4},
+  pages         = {1149--1187},
+  volume        = {116},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Quarterly journal of economics},
+  year          = {2001},
+}
+
+@Book{manning2008introduction,
+  author        = {Manning, Christopher D and Sch{\"u}tze, Hinrich and Raghavan, Prabhakar},
+  title         = {Introduction to Information Retrieval},
+  publisher     = {{Cambridge university press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2008},
+}
+
+@misc{manokhin2022awesome,
+	author = {Manokhin, Valery},
+	date-added = {2022-12-13 12:58:01 +0100},
+	date-modified = {2022-12-13 12:58:01 +0100},
+	title = {Awesome Conformal Prediction}}
+
+@Article{manski1993identification,
+  author        = {Manski, Charles F},
+  title         = {Identification of Endogenous Social Effects: {{The}} Reflection Problem},
+  number        = {3},
+  pages         = {531--542},
+  volume        = {60},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The review of economic studies},
+  year          = {1993},
+}
+
+@Article{markle2018goals,
+  author        = {Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
+  title         = {Goals as Reference Points in Marathon Running: {{A}} Novel Test of Reference Dependence},
+  number        = {1},
+  pages         = {19--50},
+  volume        = {56},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Risk and Uncertainty},
+  year          = {2018},
+}
+
+@Article{masini2021machine,
+  author        = {Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F},
+  title         = {Machine Learning Advances for Time Series Forecasting},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Economic Surveys},
+  year          = {2021},
+}
+
+@Article{mccracken2016fredmd,
+  author        = {McCracken, Michael W and Ng, Serena},
+  title         = {{{FRED-MD}}: {{A}} Monthly Database for Macroeconomic Research},
+  number        = {4},
+  pages         = {574--589},
+  volume        = {34},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Business \& Economic Statistics},
+  year          = {2016},
+}
+
+@Article{mcculloch1990logical,
+  author        = {McCulloch, Warren S and Pitts, Walter},
+  title         = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
+  number        = {1},
+  pages         = {99--115},
+  volume        = {52},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Bulletin of mathematical biology},
+  year          = {1990},
+}
+
+@Article{migut2015visualizing,
+  author        = {Migut, MA and Worring, Marcel and Veenman, Cor J},
+  title         = {Visualizing Multi-Dimensional Decision Boundaries in {{2D}}},
+  number        = {1},
+  pages         = {273--295},
+  volume        = {29},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Data Mining and Knowledge Discovery},
+  year          = {2015},
+}
+
+@Article{miller2019explanation,
+  author        = {Miller, Tim},
+  title         = {Explanation in Artificial Intelligence: {{Insights}} from the Social Sciences},
+  pages         = {1--38},
+  volume        = {267},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Artificial intelligence},
+  year          = {2019},
+}
+
+@InProceedings{miller2020strategic,
+  author        = {Miller, John and Milli, Smitha and Hardt, Moritz},
+  booktitle     = {Proceedings of the 37th {{International Conference}} on {{Machine Learning}}},
+  title         = {Strategic {{Classification}} Is {{Causal Modeling}} in {{Disguise}}},
+  eventtitle    = {International {{Conference}} on {{Machine Learning}}},
+  pages         = {6917--6926},
+  publisher     = {{PMLR}},
+  url           = {https://proceedings.mlr.press/v119/miller20b.html},
+  urldate       = {2022-11-03},
+  abstract      = {Consequential decision-making incentivizes individuals to strategically adapt their behavior to the specifics of the decision rule. While a long line of work has viewed strategic adaptation as gaming and attempted to mitigate its effects, recent work has instead sought to design classifiers that incentivize individuals to improve a desired quality. Key to both accounts is a cost function that dictates which adaptations are rational to undertake. In this work, we develop a causal framework for strategic adaptation. Our causal perspective clearly distinguishes between gaming and improvement and reveals an important obstacle to incentive design. We prove any procedure for designing classifiers that incentivize improvement must inevitably solve a non-trivial causal inference problem. We show a similar result holds for designing cost functions that satisfy the requirements of previous work. With the benefit of hindsight, our results show much of the prior work on strategic classification is causal modeling in disguise.},
+  bdsk-url-1    = {https://proceedings.mlr.press/v119/miller20b.html},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  file          = {:/Users/FA31DU/Zotero/storage/46I2QMPI/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf:;:/Users/FA31DU/Zotero/storage/NWREET6B/Miller et al. - 2020 - Strategic Classification is Causal Modeling in Dis.pdf:},
+  issn          = {2640-3498},
+  langid        = {english},
+  month         = nov,
+  year          = {2020},
+}
+
+@Article{mischel1988nature,
+  author        = {Mischel, Walter and Shoda, Yuichi and Peake, Philip K},
+  title         = {The Nature of Adolescent Competencies Predicted by Preschool Delay of Gratification.},
+  number        = {4},
+  pages         = {687},
+  volume        = {54},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of personality and social psychology},
+  year          = {1988},
+}
+
+@InProceedings{mittelstadt2019explaining,
+  author        = {Mittelstadt, Brent and Russell, Chris and Wachter, Sandra},
+  booktitle     = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
+  title         = {Explaining Explanations in {{AI}}},
+  pages         = {279--288},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2019},
+}
+
+@Book{molnar2020interpretable,
+  author        = {Molnar, Christoph},
+  title         = {Interpretable Machine Learning},
+  publisher     = {{Lulu. com}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Book{morgan2015counterfactuals,
+  author        = {Morgan, Stephen L and Winship, Christopher},
+  title         = {Counterfactuals and Causal Inference},
+  publisher     = {{Cambridge University Press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2015},
+}
+
+@Article{mosteller1951experimental,
+  author        = {Mosteller, Frederick and Nogee, Philip},
+  title         = {An Experimental Measurement of Utility},
+  number        = {5},
+  pages         = {371--404},
+  volume        = {59},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Political Economy},
+  year          = {1951},
+}
+
+@InProceedings{mothilal2020explaining,
+  author        = {Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
+  booktitle     = {Proceedings of the 2020 {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  title         = {Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations},
+  pages         = {607--617},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Book{murphy2012machine,
+  author        = {Murphy, Kevin P},
+  title         = {Machine Learning: A Probabilistic Perspective},
+  publisher     = {{MIT press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2012},
+}
+
+@Book{murphy2012machine,
+  author        = {Murphy, Kevin P},
+  title         = {Machine Learning: {{A}} Probabilistic Perspective},
+  publisher     = {{MIT press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2012},
+}
+
+@Book{murphy2022probabilistic,
+  author        = {Murphy, Kevin P},
+  title         = {Probabilistic {{Machine Learning}}: {{An}} Introduction},
+  publisher     = {{MIT Press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2022},
+}
+
+@Article{nagel1995unraveling,
+  author        = {Nagel, Rosemarie},
+  title         = {Unraveling in Guessing Games: {{An}} Experimental Study},
+  number        = {5},
+  pages         = {1313--1326},
+  volume        = {85},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The American Economic Review},
+  year          = {1995},
+}
+
+@Unpublished{navarro-martinez2021bridging,
+  author        = {Navarro-Martinez, Daniel and Wang, Xinghua},
+  title         = {Bridging the Gap between the Lab and the Field: {{Dictator}} Games and Donations},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@InProceedings{nelson2015evaluating,
+  author        = {Nelson, Kevin and Corbin, George and Anania, Mark and Kovacs, Matthew and Tobias, Jeremy and Blowers, Misty},
+  booktitle     = {2015 {{IEEE Symposium}} on {{Computational Intelligence}} for {{Security}} and {{Defense Applications}} ({{CISDA}})},
+  title         = {Evaluating Model Drift in Machine Learning Algorithms},
+  pages         = {1--8},
+  publisher     = {{IEEE}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2015},
+}
+
+@Book{nocedal2006numerical,
+  author        = {Nocedal, Jorge and Wright, Stephen},
+  title         = {Numerical Optimization},
+  publisher     = {{Springer Science \& Business Media}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2006},
+}
+
+@Misc{oecd2021artificial,
+  author        = {{OECD}},
+  title         = {Artificial {{Intelligence}}, {{Machine Learning}} and {{Big Data}} in {{Finance}}: {{Opportunities}}, {{Challenges}} and {{Implications}} for {{Policy Makers}}},
+  url           = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+  bdsk-url-1    = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Online{oecdArtificialIntelligenceMachine2021,
+  author        = {{OECD}},
+  title         = {Artificial {{Intelligence}}, {{Machine Learning}} and {{Big Data}} in {{Finance}}: {{Opportunities}}, {{Challenges}} and {{Implications}} for {{Policy Makers}}},
+  url           = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+  bdsk-url-1    = {https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  publisher     = {{OECD}},
+  year          = {2021},
+}
+
+@Book{oneil2016weapons,
+  author        = {O'Neil, Cathy},
+  title         = {Weapons of Math Destruction: {{How}} Big Data Increases Inequality and Threatens Democracy},
+  publisher     = {{Crown}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2016},
+}
+
+@Article{pace1997sparse,
+  author        = {Pace, R Kelley and Barry, Ronald},
+  title         = {Sparse Spatial Autoregressions},
+  number        = {3},
+  pages         = {291--297},
+  volume        = {33},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Statistics \& Probability Letters},
+  year          = {1997},
+}
+
+@Unpublished{parr2018matrix,
+  author        = {Parr, Terence and Howard, Jeremy},
+  title         = {The Matrix Calculus You Need for Deep Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1802.01528},
+  eprinttype    = {arxiv},
+  year          = {2018},
+}
+
+@Unpublished{pawelczyk2021carla,
+  author        = {Pawelczyk, Martin and Bielawski, Sascha and van den Heuvel, Johannes and Richter, Tobias and Kasneci, Gjergji},
+  title         = {Carla: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2108.00783},
+  eprinttype    = {arxiv},
+  year          = {2021},
+}
+
+@Book{pearl2018book,
+  author        = {Pearl, Judea and Mackenzie, Dana},
+  title         = {The Book of Why: The New Science of Cause and Effect},
+  publisher     = {{Basic books}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2018},
+}
+
+@Article{pearl2019seven,
+  author        = {Pearl, Judea},
+  title         = {The Seven Tools of Causal Inference, with Reflections on Machine Learning},
+  number        = {3},
+  pages         = {54--60},
+  volume        = {62},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Communications of the ACM},
+  year          = {2019},
+}
+
+@Article{pedregosa2011scikitlearn,
+  author        = {Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
+  title         = {Scikit-Learn: {{Machine}} Learning in {{Python}}},
+  pages         = {2825--2830},
+  volume        = {12},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {the Journal of machine Learning research},
+  year          = {2011},
+}
+
+@Book{perry2010economic,
+  author        = {Perry, George L and Tobin, James},
+  title         = {Economic {{Events}}, {{Ideas}}, and {{Policies}}: The 1960s and After},
+  publisher     = {{Brookings Institution Press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2010},
+}
+
+@Article{pfaff2008var,
+  author        = {Pfaff, Bernhard and others},
+  title         = {{{VAR}}, {{SVAR}} and {{SVEC}} Models: {{Implementation}} within {{R}} Package Vars},
+  number        = {4},
+  pages         = {1--32},
+  volume        = {27},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Statistical Software},
+  year          = {2008},
+}
+
+@Book{pindyck2014microeconomics,
+  author        = {Pindyck, Robert S and Rubinfeld, Daniel L},
+  title         = {Microeconomics},
+  publisher     = {{Pearson Education}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2014},
+}
+
+@Article{pope2011numbers,
+  author        = {Pope, Devin and Simonsohn, Uri},
+  title         = {Round Numbers as Goals: {{Evidence}} from Baseball, {{SAT}} Takers, and the Lab},
+  number        = {1},
+  pages         = {71--79},
+  volume        = {22},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Psychological science},
+  year          = {2011},
+}
+
+@InProceedings{poyiadzi2020face,
+  author        = {Poyiadzi, Rafael and Sokol, Kacper and Santos-Rodriguez, Raul and De Bie, Tijl and Flach, Peter},
+  booktitle     = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+  title         = {{{FACE}}: {{Feasible}} and Actionable Counterfactual Explanations},
+  pages         = {344--350},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Article{qu2015estimating,
+  author        = {Qu, Xi and Lee, Lung-fei},
+  title         = {Estimating a Spatial Autoregressive Model with an Endogenous Spatial Weight Matrix},
+  number        = {2},
+  pages         = {209--232},
+  volume        = {184},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of Econometrics},
+  year          = {2015},
+}
+
+@Article{rabanser2019failing,
+  author        = {Rabanser, Stephan and G{\"u}nnemann, Stephan and Lipton, Zachary},
+  title         = {Failing Loudly: {{An}} Empirical Study of Methods for Detecting Dataset Shift},
+  volume        = {32},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in Neural Information Processing Systems},
+  year          = {2019},
+}
+
+@Unpublished{raghunathan2019adversarial,
+  author        = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John C and Liang, Percy},
+  title         = {Adversarial Training Can Hurt Generalization},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1906.06032},
+  eprinttype    = {arxiv},
+  year          = {2019},
+}
+
+@Unpublished{raj2017taming,
+  author        = {Raj, Vishnu and Kalyani, Sheetal},
+  title         = {Taming Non-Stationary Bandits: {{A Bayesian}} Approach},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1707.09727},
+  eprinttype    = {arxiv},
+  year          = {2017},
+}
+
+@InProceedings{rasmussen2003gaussian,
+  author        = {Rasmussen, Carl Edward},
+  booktitle     = {Summer School on Machine Learning},
+  title         = {Gaussian Processes in Machine Learning},
+  pages         = {63--71},
+  publisher     = {{Springer}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2003},
+}
+
+@InProceedings{ribeiro2016why,
+  author        = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
+  booktitle     = {Proceedings of the 22nd {{ACM SIGKDD}} International Conference on Knowledge Discovery and Data Mining},
+  title         = {"{{Why}} Should i Trust You?" {{Explaining}} the Predictions of Any Classifier},
+  pages         = {1135--1144},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2016},
+}
+
+@Article{romer1989does,
+  author        = {Romer, Christina D and Romer, David H},
+  title         = {Does Monetary Policy Matter? {{A}} New Test in the Spirit of {{Friedman}} and {{Schwartz}}},
+  pages         = {121--170},
+  volume        = {4},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {NBER macroeconomics annual},
+  year          = {1989},
+}
+
+@Article{rudin2019stop,
+  author        = {Rudin, Cynthia},
+  title         = {Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead},
+  number        = {5},
+  pages         = {206--215},
+  volume        = {1},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Nature Machine Intelligence},
+  year          = {2019},
+}
+
+@Article{sacerdote2001peer,
+  author        = {Sacerdote, Bruce},
+  title         = {Peer Effects with Random Assignment: {{Results}} for {{Dartmouth}} Roommates},
+  number        = {2},
+  pages         = {681--704},
+  volume        = {116},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The Quarterly journal of economics},
+  year          = {2001},
+}
+
+@Article{sadinle2019least,
+  author        = {Sadinle, Mauricio and Lei, Jing and Wasserman, Larry},
+  title         = {Least Ambiguous Set-Valued Classifiers with Bounded Error Levels},
+  number        = {525},
+  pages         = {223--234},
+  volume        = {114},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  file          = {:/Users/FA31DU/Zotero/storage/YXQ8N76A/Sadinle et al. - 2019 - Least ambiguous set-valued classifiers with bounde.pdf:;:/Users/FA31DU/Zotero/storage/ZHB56F3V/01621459.2017.html:},
+  journal       = {Journal of the American Statistical Association},
+  publisher     = {{Taylor \& Francis}},
+  year          = {2019},
+}
+
+@InProceedings{satopaa2011finding,
+  author        = {Satopaa, Ville and Albrecht, Jeannie and Irwin, David and Raghavan, Barath},
+  booktitle     = {2011 31st International Conference on Distributed Computing Systems Workshops},
+  title         = {Finding a" Kneedle" in a Haystack: {{Detecting}} Knee Points in System Behavior},
+  pages         = {166--171},
+  publisher     = {{IEEE}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2011},
+}
+
+@InProceedings{schut2021generating,
+  author        = {Schut, Lisa and Key, Oscar and Mc Grath, Rory and Costabello, Luca and Sacaleanu, Bogdan and Gal, Yarin and others},
+  booktitle     = {International {{Conference}} on {{Artificial Intelligence}} and {{Statistics}}},
+  title         = {Generating {{Interpretable Counterfactual Explanations By Implicit Minimisation}} of {{Epistemic}} and {{Aleatoric Uncertainties}}},
+  pages         = {1756--1764},
+  publisher     = {{PMLR}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2021},
+}
+
+@Book{schutze2008introduction,
+  author        = {Sch{\"u}tze, Hinrich and Manning, Christopher D and Raghavan, Prabhakar},
+  title         = {Introduction to Information Retrieval},
+  publisher     = {{Cambridge University Press Cambridge}},
+  volume        = {39},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2008},
+}
+
+@Article{shafir1993reasonbased,
+  author        = {Shafir, Eldar and Simonson, Itamar and Tversky, Amos},
+  title         = {Reason-Based Choice},
+  number        = {1-2},
+  pages         = {11--36},
+  volume        = {49},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Cognition},
+  year          = {1993},
+}
+
+@Article{simonson1989choice,
+  author        = {Simonson, Itamar},
+  title         = {Choice Based on Reasons: {{The}} Case of Attraction and Compromise Effects},
+  number        = {2},
+  pages         = {158--174},
+  volume        = {16},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of consumer research},
+  year          = {1989},
+}
+
+@Article{sims1986are,
+  author        = {Sims, Christopher A and others},
+  title         = {Are Forecasting Models Usable for Policy Analysis?},
+  issue         = {Win},
+  pages         = {2--16},
+  volume        = {10},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Quarterly Review},
+  year          = {1986},
+}
+
+@InProceedings{slack2020fooling,
+  author        = {Slack, Dylan and Hilgard, Sophie and Jia, Emily and Singh, Sameer and Lakkaraju, Himabindu},
+  booktitle     = {Proceedings of the {{AAAI}}/{{ACM Conference}} on {{AI}}, {{Ethics}}, and {{Society}}},
+  title         = {Fooling Lime and Shap: {{Adversarial}} Attacks on Post Hoc Explanation Methods},
+  pages         = {180--186},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2020},
+}
+
+@Article{slack2021counterfactual,
+  author        = {Slack, Dylan and Hilgard, Anna and Lakkaraju, Himabindu and Singh, Sameer},
+  title         = {Counterfactual Explanations Can Be Manipulated},
+  volume        = {34},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Advances in Neural Information Processing Systems},
+  year          = {2021},
+}
+
+@Article{slovic1974who,
+  author        = {Slovic, Paul and Tversky, Amos},
+  title         = {Who Accepts {{Savage}}'s Axiom?},
+  number        = {6},
+  pages         = {368--373},
+  volume        = {19},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Behavioral science},
+  year          = {1974},
+}
+
+@Unpublished{spooner2021counterfactual,
+  author        = {Spooner, Thomas and Dervovic, Danial and Long, Jason and Shepard, Jon and Chen, Jiahao and Magazzeni, Daniele},
+  title         = {Counterfactual {{Explanations}} for {{Arbitrary Regression Models}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2106.15212},
+  eprinttype    = {arxiv},
+  shortjournal  = {arXiv preprint arXiv:2106.15212},
+  year          = {2021},
+}
+
+@Article{srivastava2014dropout,
+  author        = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
+  title         = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
+  number        = {1},
+  pages         = {1929--1958},
+  volume        = {15},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The journal of machine learning research},
+  year          = {2014},
+}
+
+@Unpublished{stanton2022bayesian,
+  author        = {Stanton, Samuel and Maddox, Wesley and Wilson, Andrew Gordon},
+  title         = {Bayesian {{Optimization}} with {{Conformal Coverage Guarantees}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2210.12496},
+  eprinttype    = {arxiv},
+  file          = {:/Users/FA31DU/Zotero/storage/XFGZAB9J/Stanton et al. - 2022 - Bayesian Optimization with Conformal Coverage Guar.pdf:;:/Users/FA31DU/Zotero/storage/RPWYDPVW/2210.html:},
+  year          = {2022},
+}
+
+@Article{sturm2014simple,
+  author        = {Sturm, Bob L},
+  title         = {A Simple Method to Determine If a Music Information Retrieval System Is a ``Horse''},
+  number        = {6},
+  pages         = {1636--1644},
+  volume        = {16},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {IEEE Transactions on Multimedia},
+  year          = {2014},
+}
+
+@Article{sunstein2003libertarian,
+  author        = {Sunstein, Cass R and Thaler, Richard H},
+  title         = {Libertarian Paternalism Is Not an Oxymoron},
+  pages         = {1159--1202},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {The University of Chicago Law Review},
+  year          = {2003},
+}
+
+@Book{sutton2018reinforcement,
+  author        = {Sutton, Richard S and Barto, Andrew G},
+  title         = {Reinforcement Learning: {{An}} Introduction},
+  publisher     = {{MIT press}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2018},
+}
+
+@Unpublished{szegedy2013intriguing,
+  author        = {Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
+  title         = {Intriguing Properties of Neural Networks},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1312.6199},
+  eprinttype    = {arxiv},
+  year          = {2013},
+}
+
+@Article{thaler1981empirical,
+  author        = {Thaler, Richard},
+  title         = {Some Empirical Evidence on Dynamic Inconsistency},
+  number        = {3},
+  pages         = {201--207},
+  volume        = {8},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Economics letters},
+  year          = {1981},
+}
+
+@Article{thaler2004more,
+  author        = {Thaler, Richard H and Benartzi, Shlomo},
+  title         = {Save More Tomorrow{\texttrademark}: {{Using}} Behavioral Economics to Increase Employee Saving},
+  number        = {S1},
+  pages         = {S164--S187},
+  volume        = {112},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of political Economy},
+  year          = {2004},
+}
+
+@Article{tversky1981framing,
+  author        = {Tversky, Amos and Kahneman, Daniel},
+  title         = {The Framing of Decisions and the Psychology of Choice},
+  number        = {4481},
+  pages         = {453--458},
+  volume        = {211},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Science (New York, N.Y.)},
+  shortjournal  = {science},
+  year          = {1981},
+}
+
+@Article{ungemach2011how,
+  author        = {Ungemach, Christoph and Stewart, Neil and Reimers, Stian},
+  title         = {How Incidental Values from the Environment Affect Decisions about Money, Risk, and Delay},
+  number        = {2},
+  pages         = {253--260},
+  volume        = {22},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Psychological Science},
+  year          = {2011},
+}
+
+@Unpublished{upadhyay2021robust,
+  author        = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
+  title         = {Towards {{Robust}} and {{Reliable Algorithmic Recourse}}},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2102.13620},
+  eprinttype    = {arxiv},
+  year          = {2021},
+}
+
+@InProceedings{ustun2019actionable,
+  author        = {Ustun, Berk and Spangher, Alexander and Liu, Yang},
+  booktitle     = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  title         = {Actionable Recourse in Linear Classification},
+  pages         = {10--19},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2019},
+}
+
+@Article{vanboven2000egocentric,
+  author        = {Van Boven, Leaf and Dunning, David and Loewenstein, George},
+  title         = {Egocentric Empathy Gaps between Owners and Buyers: Misperceptions of the Endowment Effect.},
+  number        = {1},
+  pages         = {66},
+  volume        = {79},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of personality and social psychology},
+  year          = {2000},
+}
+
+@Book{varshney2022trustworthy,
+  author        = {Varshney, Kush R.},
+  title         = {Trustworthy {{Machine Learning}}},
+  publisher     = {{Independently Published}},
+  address       = {{Chappaqua, NY, USA}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2022},
+}
+
+@Unpublished{verma2020counterfactual,
+  author        = {Verma, Sahil and Dickerson, John and Hines, Keegan},
+  title         = {Counterfactual Explanations for Machine Learning: {{A}} Review},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2010.10596},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Article{verstyuk2020modeling,
+  author        = {Verstyuk, Sergiy},
+  title         = {Modeling Multivariate Time Series in Economics: {{From}} Auto-Regressions to Recurrent Neural Networks},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Available at SSRN 3589337},
+  year          = {2020},
+}
+
+@Article{wachter2017counterfactual,
+  author        = {Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
+  title         = {Counterfactual Explanations without Opening the Black Box: {{Automated}} Decisions and the {{GDPR}}},
+  pages         = {841},
+  volume        = {31},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Harv. JL \& Tech.},
+  year          = {2017},
+}
+
+@Article{wang2018optimal,
+  author        = {Wang, HaiYing and Zhu, Rong and Ma, Ping},
+  title         = {Optimal Subsampling for Large Sample Logistic Regression},
+  number        = {522},
+  pages         = {829--844},
+  volume        = {113},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Journal of the American Statistical Association},
+  year          = {2018},
+}
+
+@Book{wasserman2006all,
+  author        = {Wasserman, Larry},
+  title         = {All of Nonparametric Statistics},
+  publisher     = {{Springer Science \& Business Media}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2006},
+}
+
+@Book{wasserman2013all,
+  author        = {Wasserman, Larry},
+  title         = {All of Statistics: A Concise Course in Statistical Inference},
+  publisher     = {{Springer Science \& Business Media}},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  year          = {2013},
+}
+
+@Article{widmer1996learning,
+  author        = {Widmer, Gerhard and Kubat, Miroslav},
+  title         = {Learning in the Presence of Concept Drift and Hidden Contexts},
+  number        = {1},
+  pages         = {69--101},
+  volume        = {23},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Machine learning},
+  year          = {1996},
+}
+
+@Unpublished{wilson2020case,
+  author        = {Wilson, Andrew Gordon},
+  title         = {The Case for {{Bayesian}} Deep Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {2001.10995},
+  eprinttype    = {arxiv},
+  year          = {2020},
+}
+
+@Article{witten2009penalized,
+  author        = {Witten, Daniela M and Tibshirani, Robert and Hastie, Trevor},
+  title         = {A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis},
+  number        = {3},
+  pages         = {515--534},
+  volume        = {10},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Biostatistics (Oxford, England)},
+  shortjournal  = {Biostatistics},
+  year          = {2009},
+}
+
+@Article{xu2020epidemiological,
+  author        = {Xu, Bo and Gutierrez, Bernardo and Mekaru, Sumiko and Sewalk, Kara and Goodwin, Lauren and Loskill, Alyssa and Cohn, Emily and Hswen, Yulin and Hill, Sarah C. and Cobo, Maria M and Zarebski, Alexander and Li, Sabrina and Wu, Chieh-Hsi and Hulland, Erin and Morgan, Julia and Wang, Lin and O'Brien, Katelynn and Scarpino, Samuel V. and Brownstein, John S. and Pybus, Oliver G. and Pigott, David M. and Kraemer, Moritz U. G.},
+  title         = {Epidemiological Data from the {{COVID-19}} Outbreak, Real-Time Case Information},
+  doi           = {doi.org/10.1038/s41597-020-0448-0},
+  number        = {106},
+  volume        = {7},
+  bdsk-url-1    = {https://doi.org/10.1038/s41597-020-0448-0},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Scientific Data},
+  year          = {2020},
+}
+
+@Article{yeh2009comparisons,
+  author        = {Yeh, I-Cheng and Lien, Che-hui},
+  title         = {The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients},
+  number        = {2},
+  pages         = {2473--2480},
+  volume        = {36},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Expert systems with applications},
+  year          = {2009},
+}
+
+@Article{zhang1998forecasting,
+  author        = {Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y},
+  title         = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art},
+  number        = {1},
+  pages         = {35--62},
+  volume        = {14},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {International journal of forecasting},
+  year          = {1998},
+}
+
+@Article{zhang2003time,
+  author        = {Zhang, G Peter},
+  title         = {Time Series Forecasting Using a Hybrid {{ARIMA}} and Neural Network Model},
+  pages         = {159--175},
+  volume        = {50},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {Neurocomputing},
+  year          = {2003},
+}
+
+@Unpublished{zheng2018dags,
+  author        = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
+  title         = {Dags with No Tears: {{Continuous}} Optimization for Structure Learning},
+  archiveprefix = {arXiv},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  eprint        = {1803.01422},
+  eprinttype    = {arxiv},
+  year          = {2018},
+}
+
+@Article{zhu2015optimal,
+  author        = {Zhu, Rong and Ma, Ping and Mahoney, Michael W and Yu, Bin},
+  title         = {Optimal Subsampling Approaches for Large Sample Linear Regression},
+  pages         = {arXiv--1509},
+  date-added    = {2022-12-13 12:58:01 +0100},
+  date-modified = {2022-12-13 12:58:01 +0100},
+  journal       = {arXiv},
+  year          = {2015},
+}
+
+@Article{barber2021predictive,
+  author    = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
+  title     = {Predictive inference with the jackknife+},
+  doi       = {10.1214/20-AOS1965},
+  issn      = {0090-5364, 2168-8966},
+  number    = {1},
+  pages     = {486--507},
+  urldate   = {2022-12-13},
+  volume    = {49},
+  abstract  = {This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to \$K\$-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk (Ann. Math. Artif. Intell. 74 (2015) 9–28) and we discuss connections.},
+  file      = {:Barber2021 - Predictive Inference with the Jackknife+.pdf:PDF},
+  journal   = {The Annals of Statistics},
+  keywords  = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
+  month     = feb,
+  publisher = {Institute of Mathematical Statistics},
+  year      = {2021},
+}
+
+@TechReport{chouldechova2018frontiers,
+  author        = {Chouldechova, Alexandra and Roth, Aaron},
+  title         = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
+  doi           = {10.48550/arXiv.1810.08810},
+  eprint        = {1810.08810},
+  note          = {arXiv:1810.08810 [cs, stat] type: article},
+  abstract      = {The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.},
+  archiveprefix = {arxiv},
+  file          = {:chouldechova2018frontiers - The Frontiers of Fairness in Machine Learning.pdf:PDF},
+  keywords      = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
+  month         = oct,
+  school        = {arXiv},
+  year          = {2018},
+}
+
+@Article{pawelczyk2022probabilistically,
+  author     = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
+  title      = {Probabilistically {Robust} {Recourse}: {Navigating} the {Trade}-offs between {Costs} and {Robustness} in {Algorithmic} {Recourse}},
+  file       = {:pawelczyk2022probabilistically - Probabilistically Robust Recourse_ Navigating the Trade Offs between Costs and Robustness in Algorithmic Recourse.pdf:PDF},
+  journal    = {arXiv preprint arXiv:2203.06768},
+  shorttitle = {Probabilistically {Robust} {Recourse}},
+  year       = {2022},
+}
+
+@InProceedings{stutz2022learning,
+  author   = {Stutz, David and Dvijotham, Krishnamurthy Dj and Cemgil, Ali Taylan and Doucet, Arnaud},
+  title    = {Learning {Optimal} {Conformal} {Classifiers}},
+  language = {en},
+  url      = {https://openreview.net/forum?id=t8O-4LKFVx},
+  urldate  = {2023-02-13},
+  abstract = {Modern deep learning based classifiers show very high accuracy on test data but this does not provide sufficient guarantees for safe deployment, especially in high-stake AI applications such as medical diagnosis. Usually, predictions are obtained without a reliable uncertainty estimate or a formal guarantee. Conformal prediction (CP) addresses these issues by using the classifier's predictions, e.g., its probability estimates, to predict confidence sets containing the true class with a user-specified probability. However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets. Thus, this paper explores strategies to differentiate through CP during training with the goal of training model with the conformal wrapper end-to-end. In our approach, conformal training (ConfTr), we specifically "simulate" conformalization on mini-batches during training. Compared to standard training, ConfTr reduces the average confidence set size (inefficiency) of state-of-the-art CP methods applied after training. Moreover, it allows to "shape" the confidence sets predicted at test time, which is difficult for standard CP. On experiments with several datasets, we show ConfTr can influence how inefficiency is distributed across classes, or guide the composition of confidence sets in terms of the included classes, while retaining the guarantees offered by CP.},
+  file     = {:stutz2022learning - Learning Optimal Conformal Classifiers.pdf:PDF},
+  month    = may,
+  year     = {2022},
+}
+
+@InProceedings{grathwohl2020your,
+  author   = {Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen, Joern-Henrik and Duvenaud, David and Norouzi, Mohammad and Swersky, Kevin},
+  title    = {Your classifier is secretly an energy based model and you should treat it like one},
+  language = {en},
+  url      = {https://openreview.net/forum?id=Hkxzx0NtDB},
+  urldate  = {2023-02-13},
+  abstract = {We propose to reinterpret a standard discriminative classifier of p(y{\textbar}x) as an energy based model for the joint distribution p(x, y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x{\textbar}y). Within this framework, standard discriminative architectures may be used and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, and out-of-distribution detection while also enabling our models to generate samples rivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and present an approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-art in both generative and discriminative learning within one hybrid model.},
+  file     = {:grathwohl2020your - Your Classifier Is Secretly an Energy Based Model and You Should Treat It like One.pdf:PDF},
+  month    = mar,
+  year     = {2020},
+}
+
+@Book{murphy2023probabilistic,
+  author     = {Murphy, Kevin P.},
+  date       = {2023},
+  title      = {Probabilistic machine learning: {Advanced} topics},
+  publisher  = {MIT Press},
+  shorttitle = {Probabilistic machine learning},
+}
+
+@TechReport{artelt2021evaluating,
+  author      = {Artelt, André and Vaquet, Valerie and Velioglu, Riza and Hinder, Fabian and Brinkrolf, Johannes and Schilling, Malte and Hammer, Barbara},
+  date        = {2021-07},
+  institution = {arXiv},
+  title       = {Evaluating {Robustness} of {Counterfactual} {Explanations}},
+  note        = {arXiv:2103.02354 [cs] type: article},
+  url         = {http://arxiv.org/abs/2103.02354},
+  urldate     = {2023-03-24},
+  abstract    = {Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the system's behavior. A prominent and intuitive type of explanations are counterfactual explanations. Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system. However, such explanation methods can be unstable with respect to small changes to the input -- i.e. even a small change in the input can lead to huge or arbitrary changes in the output and of the explanation. This could be problematic for counterfactual explanations, as two similar individuals might get very different explanations. Even worse, if the recommended actions differ considerably in their complexity, one would consider such unstable (counterfactual) explanations as individually unfair. In this work, we formally and empirically study the robustness of counterfactual explanations in general, as well as under different models and different kinds of perturbations. Furthermore, we propose that plausible counterfactual explanations can be used instead of closest counterfactual explanations to improve the robustness and consequently the individual fairness of counterfactual explanations.},
+  annotation  = {Comment: Rewrite paper to make things more clear; Remove one theorem \& corollary due to buggy proof},
+  file        = {:artelt2021evaluating - Evaluating Robustness of Counterfactual Explanations.pdf:PDF},
+  keywords    = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence},
+}
+
+@Article{guidotti2022counterfactual,
+  author       = {Guidotti, Riccardo},
+  date         = {2022-04},
+  journaltitle = {Data Mining and Knowledge Discovery},
+  title        = {Counterfactual explanations and how to find them: literature review and benchmarking},
+  doi          = {10.1007/s10618-022-00831-6},
+  issn         = {1573-756X},
+  language     = {en},
+  url          = {https://doi.org/10.1007/s10618-022-00831-6},
+  urldate      = {2023-03-24},
+  abstract     = {Interpretable machine learning aims at unveiling the reasons behind predictions returned by uninterpretable classifiers. One of the most valuable types of explanation consists of counterfactuals. A counterfactual explanation reveals what should have been different in an instance to observe a diverse outcome. For instance, a bank customer asks for a loan that is rejected. The counterfactual explanation consists of what should have been different for the customer in order to have the loan accepted. Recently, there has been an explosion of proposals for counterfactual explainers. The aim of this work is to survey the most recent explainers returning counterfactual explanations. We categorize explainers based on the approach adopted to return the counterfactuals, and we label them according to characteristics of the method and properties of the counterfactuals returned. In addition, we visually compare the explanations, and we report quantitative benchmarking assessing minimality, actionability, stability, diversity, discriminative power, and running time. The results make evident that the current state of the art does not provide a counterfactual explainer able to guarantee all these properties simultaneously.},
+  file         = {Full Text PDF:https\://link.springer.com/content/pdf/10.1007%2Fs10618-022-00831-6.pdf:application/pdf},
+  keywords     = {Explainable AI, Counterfactual explanations, Contrastive explanations, Interpretable machine learning},
+  shorttitle   = {Counterfactual explanations and how to find them},
+}
+
+@TechReport{mahajan2020preserving,
+  author      = {Mahajan, Divyat and Tan, Chenhao and Sharma, Amit},
+  date        = {2020-06},
+  institution = {arXiv},
+  title       = {Preserving {Causal} {Constraints} in {Counterfactual} {Explanations} for {Machine} {Learning} {Classifiers}},
+  doi         = {10.48550/arXiv.1912.03277},
+  note        = {arXiv:1912.03277 [cs, stat] type: article},
+  url         = {http://arxiv.org/abs/1912.03277},
+  urldate     = {2023-03-24},
+  abstract    = {To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as preserving causal relationships among input features and present a method that uses (partial) structural causal models to generate actionable counterfactuals. When feasibility constraints cannot be easily expressed, we consider an alternative mechanism where people can label generated CF examples on feasibility: whether it is feasible to intervene and realize the candidate CF example from the original input. To learn from this labelled feasibility data, we propose a modified variational auto encoder loss for generating CF examples that optimizes for feasibility as people interact with its output. Our experiments on Bayesian networks and the widely used ''Adult-Income'' dataset show that our proposed methods can generate counterfactual explanations that better satisfy feasibility constraints than existing methods.. Code repository can be accessed here: {\textbackslash}textit\{https://github.com/divyat09/cf-feasibility\}},
+  annotation  = {Comment: 2019 NeurIPS Workshop on Do the right thing: Machine learning and Causal Inference for improved decision making},
+  file        = {:mahajan2020preserving - Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers.pdf:PDF},
+  keywords    = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning},
+}
+
+@TechReport{antoran2023sampling,
+  author      = {Antorán, Javier and Padhy, Shreyas and Barbano, Riccardo and Nalisnick, Eric and Janz, David and Hernández-Lobato, José Miguel},
+  date        = {2023-03},
+  institution = {arXiv},
+  title       = {Sampling-based inference for large linear models, with application to linearised {Laplace}},
+  note        = {arXiv:2210.04994 [cs, stat] type: article},
+  url         = {http://arxiv.org/abs/2210.04994},
+  urldate     = {2023-03-25},
+  abstract    = {Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small datasets. We address this limitation by introducing a scalable sample-based Bayesian inference method for conjugate Gaussian multi-output linear models, together with a matching method for hyperparameter (regularisation) selection. Furthermore, we use a classic feature normalisation method (the g-prior) to resolve a previously highlighted pathology of the linearised Laplace method. Together, these contributions allow us to perform linearised neural network inference with ResNet-18 on CIFAR100 (11M parameters, 100 outputs x 50k datapoints), with ResNet-50 on Imagenet (50M parameters, 1000 outputs x 1.2M datapoints) and with a U-Net on a high-resolution tomographic reconstruction task (2M parameters, 251k output{\textasciitilde}dimensions).},
+  annotation  = {Comment: Published at ICLR 2023. This latest Arxiv version is extended with a demonstration of the proposed methods on the Imagenet dataset},
+  file        = {arXiv Fulltext PDF:https\://arxiv.org/pdf/2210.04994.pdf:application/pdf},
+  keywords    = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
+}
+
+@Misc{altmeyer2022conformal,
+  author   = {Altmeyer, Patrick},
+  date     = {2022-10},
+  title    = {{Conformal} {Prediction} in {Julia}},
+  language = {en},
+  url      = {https://www.paltmeyer.com/blog/posts/conformal-prediction/},
+  urldate  = {2023-03-27},
+  abstract = {A (very) gentle introduction to Conformal Prediction in Julia using my new package ConformalPrediction.jl.},
+}
+
+@Comment{jabref-meta: databaseType:biblatex;}
diff --git a/docs/index.html b/docs/index.html
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+ <span class="menu-text"><span class="chapter-number">1</span>&nbsp; <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span></a>
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+   
+  <ul>
+  <li><a href="#preface" id="toc-preface" class="nav-link active" data-scroll-target="#preface">Preface</a></li>
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+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Conformal Counterfactual Explanations</h1>
+<p class="subtitle lead">Online Companion</p>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    <div>
+    <div class="quarto-title-meta-heading">Author</div>
+    <div class="quarto-title-meta-contents">
+             <p>Patrick Altmeyer </p>
+          </div>
+  </div>
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">March 30, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<section id="preface" class="level1 unnumbered">
+<h1 class="unnumbered">Preface</h1>
+
+
+</section>
+
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+ <span class="menu-text">&lt;span class=&#39;chapter-number&#39;&gt;1&lt;/span&gt;  &lt;span class=&#39;chapter-title&#39;&gt;High-Fidelity Counterfactual Explanations through Conformal Prediction&lt;/span&gt;</span></a>
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+<h1 class="title"><span class="chapter-number">1</span>  <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></h1>
+<p class="subtitle lead">Research Proposal</p>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+  
+    
+  </div>
+  
+<div>
+  <div class="abstract">
+    <div class="abstract-title">Abstract</div>
+    <p>We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</p>
+  </div>
+</div>
+
+</header>
+<nav id="TOC" role="doc-toc">
+    <h2 id="toc-title">Table of contents</h2>
+   
+  <ul>
+  <li><a href="#motivation" id="toc-motivation"><span class="header-section-number">1.1</span> Motivation</a>
+  <ul>
+  <li><a href="#counterfactual-explanations-or-adversarial-examples" id="toc-counterfactual-explanations-or-adversarial-examples"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</a></li>
+  <li><a href="#sec-fidelity" id="toc-sec-fidelity"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</a></li>
+  </ul></li>
+  <li><a href="#conformal-counterfactual-explanations" id="toc-conformal-counterfactual-explanations"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</a>
+  <ul>
+  <li><a href="#minimizing-predictive-uncertainty" id="toc-minimizing-predictive-uncertainty"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</a></li>
+  <li><a href="#background-on-conformal-prediction" id="toc-background-on-conformal-prediction"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</a></li>
+  <li><a href="#generating-conformal-counterfactuals" id="toc-generating-conformal-counterfactuals"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</a></li>
+  </ul></li>
+  <li><a href="#experiments" id="toc-experiments"><span class="header-section-number">1.3</span> Experiments</a>
+  <ul>
+  <li><a href="#research-questions" id="toc-research-questions"><span class="header-section-number">1.3.1</span> Research Questions</a></li>
+  </ul></li>
+  <li><a href="#references" id="toc-references"><span class="header-section-number">1.4</span> References</a></li>
+  </ul>
+</nav>
+<section id="motivation" class="level2" data-number="1.1">
+<h2 data-number="1.1"><span class="header-section-number">1.1</span> Motivation</h2>
+<p>Counterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse.</p>
+<section id="counterfactual-explanations-or-adversarial-examples" class="level3" data-number="1.1.1">
+<h3 data-number="1.1.1"><span class="header-section-number">1.1.1</span> Counterfactual Explanations or Adversarial Examples?</h3>
+<p>Most state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs <span class="math inline">\(x\in\mathcal{X}\)</span> into a black-box model <span class="math inline">\(f: \mathcal{X} \mapsto \mathcal{Y}\)</span> in order to change the model output <span class="math inline">\(f(x)\)</span> to some pre-specified target value <span class="math inline">\(t\in\mathcal{Y}\)</span>. Formally, this boils down to defining some loss function <span class="math inline">\(\ell(f(x),t)\)</span> and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In <a href="#fig-adv">Figure <span class="quarto-unresolved-ref">fig-adv</span></a>, for example, generic counterfactual search as in <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> has been applied to MNIST data.</p>
+<div id="fig-adv" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/you_may_not_like_it.png" class="quarto-discovered-preview-image img-fluid" /></p>
+<p><figcaption>Figure 1.1: You may not like it, but this is what stripped-down counterfactuals look like. Here we have used <span class="citation" data-cites="wachter2017counterfactual">Wachter, Mittelstadt, and Russell (<a href="#ref-wachter2017counterfactual" role="doc-biblioref">2017</a>)</span> to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).</figcaption></p>
+</figure>
+</div>
+<p>The crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be “plausible”, “realistic” or “feasible”. To fulfil this latter goal, researchers have come up with a myriad of ways. <span class="citation" data-cites="joshi2019realistic">Joshi et al. (<a href="#ref-joshi2019realistic" role="doc-biblioref">2019</a>)</span> were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, <span class="citation" data-cites="poyiadzi2020face">Poyiadzi et al. (<a href="#ref-poyiadzi2020face" role="doc-biblioref">2020</a>)</span> use density … Finally, <span class="citation" data-cites="karimi2021algorithmic">Karimi, Schölkopf, and Valera (<a href="#ref-karimi2021algorithmic" role="doc-biblioref">2021</a>)</span> argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include … All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB).</p>
+<div id="def-plausible" class="theorem definition">
+<p><span class="theorem-title"><strong>Definition 1.1 (Plausible Counterfactuals) </strong></span>Formally, if <span class="math inline">\(x \sim \mathcal{X}\)</span> and for the corresponding counterfactual we have <span class="math inline">\(x^{\prime}\sim\mathcal{X}^{\prime}\)</span>, then for <span class="math inline">\(x^{\prime}\)</span> to be considered a plausible counterfactual, we need: <span class="math inline">\(\mathcal{X} \approxeq \mathcal{X}^{\prime}\)</span>.</p>
+</div>
+<p>In the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn <span class="math inline">\(\mathcal{X}\)</span>, for example, has very serious implications for the generated counterfactuals. <a href="#fig-latent">Figure <span class="quarto-unresolved-ref">fig-latent</span></a> compares the results of applying REVISE <span class="citation" data-cites="joshi2019realistic">(<a href="#ref-joshi2019realistic" role="doc-biblioref">Joshi et al. 2019</a>)</span> to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in <span class="math inline">\(\mathcal{X}\)</span> and hence the counterfactual search in the learned latent space is doomed.</p>
+<div id="fig-latent" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img src="www/mnist_9to4_latent.png" class="img-fluid" /></p>
+<p><figcaption>Figure 1.2: Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).</figcaption></p>
+</figure>
+</div>
+<blockquote>
+<p>Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).</p>
+</blockquote>
+<ul>
+<li>Latent can be manipulated:
+<ul>
+<li>train biased model</li>
+<li>train VAE with biased variable removed/attacked (use Boston housing dataset)</li>
+<li>hypothesis: will generate bias-free explanations</li>
+</ul></li>
+</ul>
+</section>
+<section id="sec-fidelity" class="level3" data-number="1.1.2">
+<h3 data-number="1.1.2"><span class="header-section-number">1.1.2</span> From Plausible to High-Fidelity Counterfactuals</h3>
+<p>In light of the findings, we propose to generally avoid using surrogate models to learn <span class="math inline">\(\mathcal{X}\)</span> in the context of Counterfactual Explanations.</p>
+<div id="prp-surrogate" class="theorem proposition">
+<p><span class="theorem-title"><strong>Proposition 1.1 (Avoid Surrogates) </strong></span>Since we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.</p>
+</div>
+<p>In cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model <span class="citation" data-cites="molnar2020interpretable">(<a href="#ref-molnar2020interpretable" role="doc-biblioref">Molnar 2020</a>)</span>. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model.</p>
+<p>In the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual <span class="math inline">\(x^{\prime}\)</span> is considered valid if and only if <span class="math inline">\(f(x^{\prime})=t\)</span>, where <span class="math inline">\(t\)</span> denote some target outcome.</p>
+<p>Does fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual <span class="math inline">\(x^{\prime}\)</span> complies with the class-conditional distribution <span class="math inline">\(\mathcal{X}_{\theta} = p_{\theta}(X|y)\)</span> where <span class="math inline">\(\theta\)</span> denote the black-box model parameters.</p>
+<div id="def-fidele" class="theorem definition">
+<p><span class="theorem-title"><strong>Definition 1.2 (High-Fidelity Counterfactuals) </strong></span>Let <span class="math inline">\(\mathcal{X}_{\theta}|y = p_{\theta}(X|y)\)</span> denote the class-conditional distribution of <span class="math inline">\(X\)</span> defined by <span class="math inline">\(\theta\)</span>. Then for <span class="math inline">\(x^{\prime}\)</span> to be considered a high-fidelity counterfactual, we need: <span class="math inline">\(\mathcal{X}_{\theta}|t \approxeq \mathcal{X}^{\prime}\)</span> where <span class="math inline">\(t\)</span> denotes the target outcome.</p>
+</div>
+<p>In order to assess the fidelity of counterfactuals, we propose the following two-step procedure:</p>
+<ol type="1">
+<li>Generate samples <span class="math inline">\(X_{\theta}|y\)</span> and <span class="math inline">\(X^{\prime}\)</span> from <span class="math inline">\(\mathcal{X}_{\theta}|t\)</span> and <span class="math inline">\(\mathcal{X}^{\prime}\)</span>, respectively.</li>
+<li>Compute the Maximum Mean Discrepancy (MMD) between <span class="math inline">\(X_{\theta}|y\)</span> and <span class="math inline">\(X^{\prime}\)</span>.</li>
+</ol>
+<p>If the computed value is different from zero, we can reject the null-hypothesis of fidelity.</p>
+<blockquote>
+<p>Two challenges here: 1) implementing the sampling procedure in <span class="citation" data-cites="grathwohl2020your">Grathwohl et al. (<a href="#ref-grathwohl2020your" role="doc-biblioref">2020</a>)</span>; 2) it is unclear if MMD is really the right way to measure this.</p>
+</blockquote>
+</section>
+</section>
+<section id="conformal-counterfactual-explanations" class="level2" data-number="1.2">
+<h2 data-number="1.2"><span class="header-section-number">1.2</span> Conformal Counterfactual Explanations</h2>
+<p>In <a href="#sec-fidelity"><span class="quarto-unresolved-ref">sec-fidelity</span></a>, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models.</p>
+<section id="minimizing-predictive-uncertainty" class="level3" data-number="1.2.1">
+<h3 data-number="1.2.1"><span class="header-section-number">1.2.1</span> Minimizing Predictive Uncertainty</h3>
+<p><span class="citation" data-cites="schut2021generating">Schut et al. (<a href="#ref-schut2021generating" role="doc-biblioref">2021</a>)</span> demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, <span class="citation" data-cites="antoran2020getting">Antorán et al. (<a href="#ref-antoran2020getting" role="doc-biblioref">2020</a>)</span> …</p>
+<ul>
+<li>Problem: restricted to Bayesian models.</li>
+<li>Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction.</li>
+</ul>
+</section>
+<section id="background-on-conformal-prediction" class="level3" data-number="1.2.2">
+<h3 data-number="1.2.2"><span class="header-section-number">1.2.2</span> Background on Conformal Prediction</h3>
+<ul>
+<li>Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.</li>
+<li>Conformal prediction is instance-based. So is CE.</li>
+<li>Take any fitted model and turn it into a conformal model using calibration data.</li>
+<li>Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway.</li>
+</ul>
+<blockquote>
+<p>Does the coverage guarantee carry over to counterfactuals?</p>
+</blockquote>
+</section>
+<section id="generating-conformal-counterfactuals" class="level3" data-number="1.2.3">
+<h3 data-number="1.2.3"><span class="header-section-number">1.2.3</span> Generating Conformal Counterfactuals</h3>
+<p>While Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, <span class="citation" data-cites="stutz2022learning">Stutz et al. (<a href="#ref-stutz2022learning" role="doc-biblioref">2022</a>)</span> introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search …</p>
+<blockquote>
+<p>Challenge: still need to implement these loss functions.</p>
+</blockquote>
+</section>
+</section>
+<section id="experiments" class="level2" data-number="1.3">
+<h2 data-number="1.3"><span class="header-section-number">1.3</span> Experiments</h2>
+<section id="research-questions" class="level3" data-number="1.3.1">
+<h3 data-number="1.3.1"><span class="header-section-number">1.3.1</span> Research Questions</h3>
+<ul>
+<li><p>Is CP alone enough to ensure realistic counterfactuals?</p></li>
+<li><p>Do counterfactuals improve further as the models get better?</p></li>
+<li><p>Do counterfactuals get more realistic as coverage</p></li>
+<li><p>What happens as we vary coverage and setsize?</p></li>
+<li><p>What happens as we improve the model robustness?</p></li>
+<li><p>What happens as we improve the model’s ability to incorporate predictive uncertainty (deep ensemble, laplace)?</p></li>
+<li><p>What happens if we combine with DiCE, ClaPROAR, Gravitational?</p></li>
+<li><p>What about CE robustness to endogenous shifts <span class="citation" data-cites="altmeyer2023endogenous">(<a href="#ref-altmeyer2023endogenous" role="doc-biblioref">Altmeyer et al. 2023</a>)</span>?</p></li>
+<li><p>Benchmarking:</p>
+<ul>
+<li>add PROBE <span class="citation" data-cites="pawelczyk2022probabilistically">(<a href="#ref-pawelczyk2022probabilistically" role="doc-biblioref">Pawelczyk et al. 2022</a>)</span> into the mix.</li>
+<li>compare travel costs to domain shits.</li>
+</ul></li>
+</ul>
+<blockquote>
+<p>Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace?</p>
+</blockquote>
+</section>
+</section>
+<section id="references" class="level2" data-number="1.4">
+<h2 data-number="1.4"><span class="header-section-number">1.4</span> References</h2>
+<div id="quarto-navigation-envelope" class="hidden">
+<p><span class="hidden" data-render-id="quarto-int-sidebar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-navbar-title">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-int-next"><span class="chapter-number">2</span>  <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-prev">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/index.html">Preface</span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/proposal.html"><span class="chapter-number">1</span>  <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/intro.html"><span class="chapter-number">2</span>  <span class="chapter-title"><code>ConformalGenerator</code></span></span> <span class="hidden" data-render-id="quarto-int-sidebar:/notebooks/references.html">References</span></p>
+</div>
+<div id="quarto-meta-markdown" class="hidden">
+<p><span class="hidden" data-render-id="quarto-metatitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>  <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-twittercardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>  <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-ogcardtitle">Conformal Counterfactual Explanations - <span class="chapter-number">1</span>  <span class="chapter-title">High-Fidelity Counterfactual Explanations through Conformal Prediction</span></span> <span class="hidden" data-render-id="quarto-metasitename">Conformal Counterfactual Explanations</span> <span class="hidden" data-render-id="quarto-twittercarddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span> <span class="hidden" data-render-id="quarto-ogcardddesc">We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.</span></p>
+</div>
+<div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-altmeyer2023endogenous" class="csl-entry" role="listitem">
+Altmeyer, Patrick, Giovan Angela, Aleksander Buszydlik, Karol Dobiczek, Arie van Deursen, and Cynthia Liem. 2023. <span>“Endogenous <span>Macrodynamics</span> in <span>Algorithmic</span> <span>Recourse</span>.”</span> In <em>First <span>IEEE</span> <span>Conference</span> on <span>Secure</span> and <span>Trustworthy</span> <span>Machine</span> <span>Learning</span></em>.
+</div>
+<div id="ref-antoran2020getting" class="csl-entry" role="listitem">
+Antorán, Javier, Umang Bhatt, Tameem Adel, Adrian Weller, and José Miguel Hernández-Lobato. 2020. <span>“Getting a Clue: <span>A</span> Method for Explaining Uncertainty Estimates.”</span> <a href="https://arxiv.org/abs/2006.06848">https://arxiv.org/abs/2006.06848</a>.
+</div>
+<div id="ref-grathwohl2020your" class="csl-entry" role="listitem">
+Grathwohl, Will, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky. 2020. <span>“Your Classifier Is Secretly an Energy Based Model and You Should Treat It Like One.”</span> In. <a href="https://openreview.net/forum?id=Hkxzx0NtDB">https://openreview.net/forum?id=Hkxzx0NtDB</a>.
+</div>
+<div id="ref-joshi2019realistic" class="csl-entry" role="listitem">
+Joshi, Shalmali, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh. 2019. <span>“Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.”</span> <a href="https://arxiv.org/abs/1907.09615">https://arxiv.org/abs/1907.09615</a>.
+</div>
+<div id="ref-karimi2021algorithmic" class="csl-entry" role="listitem">
+Karimi, Amir-Hossein, Bernhard Schölkopf, and Isabel Valera. 2021. <span>“Algorithmic Recourse: From Counterfactual Explanations to Interventions.”</span> In <em>Proceedings of the 2021 <span>ACM Conference</span> on <span>Fairness</span>, <span>Accountability</span>, and <span>Transparency</span></em>, 353–62.
+</div>
+<div id="ref-molnar2020interpretable" class="csl-entry" role="listitem">
+Molnar, Christoph. 2020. <em>Interpretable Machine Learning</em>. <span>Lulu. com</span>.
+</div>
+<div id="ref-pawelczyk2022probabilistically" class="csl-entry" role="listitem">
+Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. 2022. <span>“Probabilistically <span>Robust</span> <span>Recourse</span>: <span>Navigating</span> the <span>Trade</span>-Offs Between <span>Costs</span> and <span>Robustness</span> in <span>Algorithmic</span> <span>Recourse</span>.”</span> <em>arXiv Preprint arXiv:2203.06768</em>.
+</div>
+<div id="ref-poyiadzi2020face" class="csl-entry" role="listitem">
+Poyiadzi, Rafael, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. 2020. <span>“<span>FACE</span>: <span>Feasible</span> and Actionable Counterfactual Explanations.”</span> In <em>Proceedings of the <span>AAAI</span>/<span>ACM Conference</span> on <span>AI</span>, <span>Ethics</span>, and <span>Society</span></em>, 344–50.
+</div>
+<div id="ref-schut2021generating" class="csl-entry" role="listitem">
+Schut, Lisa, Oscar Key, Rory Mc Grath, Luca Costabello, Bogdan Sacaleanu, Yarin Gal, et al. 2021. <span>“Generating <span>Interpretable Counterfactual Explanations By Implicit Minimisation</span> of <span>Epistemic</span> and <span>Aleatoric Uncertainties</span>.”</span> In <em>International <span>Conference</span> on <span>Artificial Intelligence</span> and <span>Statistics</span></em>, 1756–64. <span>PMLR</span>.
+</div>
+<div id="ref-stutz2022learning" class="csl-entry" role="listitem">
+Stutz, David, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, and Arnaud Doucet. 2022. <span>“Learning <span>Optimal</span> <span>Conformal</span> <span>Classifiers</span>.”</span> In. <a href="https://openreview.net/forum?id=t8O-4LKFVx">https://openreview.net/forum?id=t8O-4LKFVx</a>.
+</div>
+<div id="ref-wachter2017counterfactual" class="csl-entry" role="listitem">
+Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2017. <span>“Counterfactual Explanations Without Opening the Black Box: <span>Automated</span> Decisions and the <span>GDPR</span>.”</span> <em>Harv. JL &amp; Tech.</em> 31: 841.
+</div>
+</div>
+</section>
+
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+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>
+<nav class="page-navigation">
+  <div class="nav-page nav-page-previous">
+      <a  href="/index.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Preface</span>
+      </a>          
+  </div>
+  <div class="nav-page nav-page-next">
+      <a  href="/notebooks/intro.html" class="pagination-link">
+        <span class="nav-page-text"><span class='chapter-number'>2</span>  <span class='chapter-title'>`ConformalGenerator`</span></span> <i class="bi bi-arrow-right-short"></i>
+      </a>
+  </div>
+</nav>
+</div> <!-- /content -->
+
+</body>
+
+</html>
\ No newline at end of file
diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css
new file mode 100644
index 00000000..94f19404
--- /dev/null
+++ b/docs/site_libs/bootstrap/bootstrap-icons.css
@@ -0,0 +1,2018 @@
+@font-face {
+  font-display: block;
+  font-family: "bootstrap-icons";
+  src: 
+url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff");
+}
+
+.bi::before,
+[class^="bi-"]::before,
+[class*=" bi-"]::before {
+  display: inline-block;
+  font-family: bootstrap-icons !important;
+  font-style: normal;
+  font-weight: normal !important;
+  font-variant: normal;
+  text-transform: none;
+  line-height: 1;
+  vertical-align: -.125em;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+.bi-123::before { content: "\f67f"; }
+.bi-alarm-fill::before { content: "\f101"; }
+.bi-alarm::before { content: "\f102"; }
+.bi-align-bottom::before { content: "\f103"; }
+.bi-align-center::before { content: "\f104"; }
+.bi-align-end::before { content: "\f105"; }
+.bi-align-middle::before { content: "\f106"; }
+.bi-align-start::before { content: "\f107"; }
+.bi-align-top::before { content: "\f108"; }
+.bi-alt::before { content: "\f109"; }
+.bi-app-indicator::before { content: "\f10a"; }
+.bi-app::before { content: "\f10b"; }
+.bi-archive-fill::before { content: "\f10c"; }
+.bi-archive::before { content: "\f10d"; }
+.bi-arrow-90deg-down::before { content: "\f10e"; }
+.bi-arrow-90deg-left::before { content: "\f10f"; }
+.bi-arrow-90deg-right::before { content: "\f110"; }
+.bi-arrow-90deg-up::before { content: "\f111"; }
+.bi-arrow-bar-down::before { content: "\f112"; }
+.bi-arrow-bar-left::before { content: "\f113"; }
+.bi-arrow-bar-right::before { content: "\f114"; }
+.bi-arrow-bar-up::before { content: "\f115"; }
+.bi-arrow-clockwise::before { content: "\f116"; }
+.bi-arrow-counterclockwise::before { content: "\f117"; }
+.bi-arrow-down-circle-fill::before { content: "\f118"; }
+.bi-arrow-down-circle::before { content: "\f119"; }
+.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
+.bi-arrow-down-left-circle::before { content: "\f11b"; }
+.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
+.bi-arrow-down-left-square::before { content: "\f11d"; }
+.bi-arrow-down-left::before { content: "\f11e"; }
+.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
+.bi-arrow-down-right-circle::before { content: "\f120"; }
+.bi-arrow-down-right-square-fill::before { content: "\f121"; }
+.bi-arrow-down-right-square::before { content: "\f122"; }
+.bi-arrow-down-right::before { content: "\f123"; }
+.bi-arrow-down-short::before { content: "\f124"; }
+.bi-arrow-down-square-fill::before { content: "\f125"; }
+.bi-arrow-down-square::before { content: "\f126"; }
+.bi-arrow-down-up::before { content: "\f127"; }
+.bi-arrow-down::before { content: "\f128"; }
+.bi-arrow-left-circle-fill::before { content: "\f129"; }
+.bi-arrow-left-circle::before { content: "\f12a"; }
+.bi-arrow-left-right::before { content: "\f12b"; }
+.bi-arrow-left-short::before { content: "\f12c"; }
+.bi-arrow-left-square-fill::before { content: "\f12d"; }
+.bi-arrow-left-square::before { content: "\f12e"; }
+.bi-arrow-left::before { content: "\f12f"; }
+.bi-arrow-repeat::before { content: "\f130"; }
+.bi-arrow-return-left::before { content: "\f131"; }
+.bi-arrow-return-right::before { content: "\f132"; }
+.bi-arrow-right-circle-fill::before { content: "\f133"; }
+.bi-arrow-right-circle::before { content: "\f134"; }
+.bi-arrow-right-short::before { content: "\f135"; }
+.bi-arrow-right-square-fill::before { content: "\f136"; }
+.bi-arrow-right-square::before { content: "\f137"; }
+.bi-arrow-right::before { content: "\f138"; }
+.bi-arrow-up-circle-fill::before { content: "\f139"; }
+.bi-arrow-up-circle::before { content: "\f13a"; }
+.bi-arrow-up-left-circle-fill::before { content: "\f13b"; }
+.bi-arrow-up-left-circle::before { content: "\f13c"; }
+.bi-arrow-up-left-square-fill::before { content: "\f13d"; }
+.bi-arrow-up-left-square::before { content: "\f13e"; }
+.bi-arrow-up-left::before { content: "\f13f"; }
+.bi-arrow-up-right-circle-fill::before { content: "\f140"; }
+.bi-arrow-up-right-circle::before { content: "\f141"; }
+.bi-arrow-up-right-square-fill::before { content: "\f142"; }
+.bi-arrow-up-right-square::before { content: "\f143"; }
+.bi-arrow-up-right::before { content: "\f144"; }
+.bi-arrow-up-short::before { content: "\f145"; }
+.bi-arrow-up-square-fill::before { content: "\f146"; }
+.bi-arrow-up-square::before { content: "\f147"; }
+.bi-arrow-up::before { content: "\f148"; }
+.bi-arrows-angle-contract::before { content: "\f149"; }
+.bi-arrows-angle-expand::before { content: "\f14a"; }
+.bi-arrows-collapse::before { content: "\f14b"; }
+.bi-arrows-expand::before { content: "\f14c"; }
+.bi-arrows-fullscreen::before { content: "\f14d"; }
+.bi-arrows-move::before { content: "\f14e"; }
+.bi-aspect-ratio-fill::before { content: "\f14f"; }
+.bi-aspect-ratio::before { content: "\f150"; }
+.bi-asterisk::before { content: "\f151"; }
+.bi-at::before { content: "\f152"; }
+.bi-award-fill::before { content: "\f153"; }
+.bi-award::before { content: "\f154"; }
+.bi-back::before { content: "\f155"; }
+.bi-backspace-fill::before { content: "\f156"; }
+.bi-backspace-reverse-fill::before { content: "\f157"; }
+.bi-backspace-reverse::before { content: "\f158"; }
+.bi-backspace::before { content: "\f159"; }
+.bi-badge-3d-fill::before { content: "\f15a"; }
+.bi-badge-3d::before { content: "\f15b"; }
+.bi-badge-4k-fill::before { content: "\f15c"; }
+.bi-badge-4k::before { content: "\f15d"; }
+.bi-badge-8k-fill::before { content: "\f15e"; }
+.bi-badge-8k::before { content: "\f15f"; }
+.bi-badge-ad-fill::before { content: "\f160"; }
+.bi-badge-ad::before { content: "\f161"; }
+.bi-badge-ar-fill::before { content: "\f162"; }
+.bi-badge-ar::before { content: "\f163"; }
+.bi-badge-cc-fill::before { content: "\f164"; }
+.bi-badge-cc::before { content: "\f165"; }
+.bi-badge-hd-fill::before { content: "\f166"; }
+.bi-badge-hd::before { content: "\f167"; }
+.bi-badge-tm-fill::before { content: "\f168"; }
+.bi-badge-tm::before { content: "\f169"; }
+.bi-badge-vo-fill::before { content: "\f16a"; }
+.bi-badge-vo::before { content: "\f16b"; }
+.bi-badge-vr-fill::before { content: "\f16c"; }
+.bi-badge-vr::before { content: "\f16d"; }
+.bi-badge-wc-fill::before { content: "\f16e"; }
+.bi-badge-wc::before { content: "\f16f"; }
+.bi-bag-check-fill::before { content: "\f170"; }
+.bi-bag-check::before { content: "\f171"; }
+.bi-bag-dash-fill::before { content: "\f172"; }
+.bi-bag-dash::before { content: "\f173"; }
+.bi-bag-fill::before { content: "\f174"; }
+.bi-bag-plus-fill::before { content: "\f175"; }
+.bi-bag-plus::before { content: "\f176"; }
+.bi-bag-x-fill::before { content: "\f177"; }
+.bi-bag-x::before { content: "\f178"; }
+.bi-bag::before { content: "\f179"; }
+.bi-bar-chart-fill::before { content: "\f17a"; }
+.bi-bar-chart-line-fill::before { content: "\f17b"; }
+.bi-bar-chart-line::before { content: "\f17c"; }
+.bi-bar-chart-steps::before { content: "\f17d"; }
+.bi-bar-chart::before { content: "\f17e"; }
+.bi-basket-fill::before { content: "\f17f"; }
+.bi-basket::before { content: "\f180"; }
+.bi-basket2-fill::before { content: "\f181"; }
+.bi-basket2::before { content: "\f182"; }
+.bi-basket3-fill::before { content: "\f183"; }
+.bi-basket3::before { content: "\f184"; }
+.bi-battery-charging::before { content: "\f185"; }
+.bi-battery-full::before { content: "\f186"; }
+.bi-battery-half::before { content: "\f187"; }
+.bi-battery::before { content: "\f188"; }
+.bi-bell-fill::before { content: "\f189"; }
+.bi-bell::before { content: "\f18a"; }
+.bi-bezier::before { content: "\f18b"; }
+.bi-bezier2::before { content: "\f18c"; }
+.bi-bicycle::before { content: "\f18d"; }
+.bi-binoculars-fill::before { content: "\f18e"; }
+.bi-binoculars::before { content: "\f18f"; }
+.bi-blockquote-left::before { content: "\f190"; }
+.bi-blockquote-right::before { content: "\f191"; }
+.bi-book-fill::before { content: "\f192"; }
+.bi-book-half::before { content: "\f193"; }
+.bi-book::before { content: "\f194"; }
+.bi-bookmark-check-fill::before { content: "\f195"; }
+.bi-bookmark-check::before { content: "\f196"; }
+.bi-bookmark-dash-fill::before { content: "\f197"; }
+.bi-bookmark-dash::before { content: "\f198"; }
+.bi-bookmark-fill::before { content: "\f199"; }
+.bi-bookmark-heart-fill::before { content: "\f19a"; }
+.bi-bookmark-heart::before { content: "\f19b"; }
+.bi-bookmark-plus-fill::before { content: "\f19c"; }
+.bi-bookmark-plus::before { content: "\f19d"; }
+.bi-bookmark-star-fill::before { content: "\f19e"; }
+.bi-bookmark-star::before { content: "\f19f"; }
+.bi-bookmark-x-fill::before { content: "\f1a0"; }
+.bi-bookmark-x::before { content: "\f1a1"; }
+.bi-bookmark::before { content: "\f1a2"; }
+.bi-bookmarks-fill::before { content: "\f1a3"; }
+.bi-bookmarks::before { content: "\f1a4"; }
+.bi-bookshelf::before { content: "\f1a5"; }
+.bi-bootstrap-fill::before { content: "\f1a6"; }
+.bi-bootstrap-reboot::before { content: "\f1a7"; }
+.bi-bootstrap::before { content: "\f1a8"; }
+.bi-border-all::before { content: "\f1a9"; }
+.bi-border-bottom::before { content: "\f1aa"; }
+.bi-border-center::before { content: "\f1ab"; }
+.bi-border-inner::before { content: "\f1ac"; }
+.bi-border-left::before { content: "\f1ad"; }
+.bi-border-middle::before { content: "\f1ae"; }
+.bi-border-outer::before { content: "\f1af"; }
+.bi-border-right::before { content: "\f1b0"; }
+.bi-border-style::before { content: "\f1b1"; }
+.bi-border-top::before { content: "\f1b2"; }
+.bi-border-width::before { content: "\f1b3"; }
+.bi-border::before { content: "\f1b4"; }
+.bi-bounding-box-circles::before { content: "\f1b5"; }
+.bi-bounding-box::before { content: "\f1b6"; }
+.bi-box-arrow-down-left::before { content: "\f1b7"; }
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+.bi-box-arrow-down::before { content: "\f1b9"; }
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+.bi-box-arrow-in-down-right::before { content: "\f1bb"; }
+.bi-box-arrow-in-down::before { content: "\f1bc"; }
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+.bi-box-arrow-in-up-right::before { content: "\f1c0"; }
+.bi-box-arrow-in-up::before { content: "\f1c1"; }
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+.bi-box-arrow-up-left::before { content: "\f1c4"; }
+.bi-box-arrow-up-right::before { content: "\f1c5"; }
+.bi-box-arrow-up::before { content: "\f1c6"; }
+.bi-box-seam::before { content: "\f1c7"; }
+.bi-box::before { content: "\f1c8"; }
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+.bi-briefcase-fill::before { content: "\f1cb"; }
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+.bi-brightness-high-fill::before { content: "\f1d1"; }
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+.bi-building::before { content: "\f1dd"; }
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diff --git a/docs/site_libs/bootstrap/bootstrap-icons.woff b/docs/site_libs/bootstrap/bootstrap-icons.woff
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diff --git a/docs/site_libs/bootstrap/bootstrap.min.css b/docs/site_libs/bootstrap/bootstrap.min.css
new file mode 100644
index 00000000..840db43e
--- /dev/null
+++ b/docs/site_libs/bootstrap/bootstrap.min.css
@@ -0,0 +1,10 @@
+/*!
+ * Bootstrap v5.1.3 (https://getbootstrap.com/)
+ * Copyright 2011-2021 The Bootstrap Authors
+ * Copyright 2011-2021 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+ */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: #f0ad4e;--bs-yellow: #ff7518;--bs-green: #3fb618;--bs-teal: #20c997;--bs-cyan: #9954bb;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #373a3c;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #373a3c;--bs-gray-900: #212529;--bs-default: #373a3c;--bs-primary: #2780e3;--bs-secondary: #373a3c;--bs-success: #3fb618;--bs-info: #9954bb;--bs-warning: #ff7518;--bs-danger: #ff0039;--bs-light: #f8f9fa;--bs-dark: #373a3c;--bs-default-rgb: 55, 58, 60;--bs-primary-rgb: 39, 128, 227;--bs-secondary-rgb: 55, 58, 60;--bs-success-rgb: 63, 182, 24;--bs-info-rgb: 153, 84, 187;--bs-warning-rgb: 255, 117, 24;--bs-danger-rgb: 255, 0, 57;--bs-light-rgb: 248, 249, 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Q="x"===y?mt:bt,G="x"===y?gt:_t,Z=E[w],J=Z+g[Q],tt=Z-g[G],et=oe(f?ne(J,K):J,Z,f?ie(tt,X):tt);E[w]=et,C[w]=et-Z}}e.modifiersData[n]=C}},requiresIfExists:["offset"]};function Me(t,e,i){void 0===i&&(i=!1);var n=zt(e);zt(e)&&function(t){var e=t.getBoundingClientRect();e.width,t.offsetWidth,e.height,t.offsetHeight}(e);var s,o,r=Gt(e),a=Vt(t),l={scrollLeft:0,scrollTop:0},c={x:0,y:0};return(n||!n&&!i)&&(("body"!==Rt(e)||we(r))&&(l=(s=e)!==Wt(s)&&zt(s)?{scrollLeft:(o=s).scrollLeft,scrollTop:o.scrollTop}:ve(s)),zt(e)?((c=Vt(e)).x+=e.clientLeft,c.y+=e.clientTop):r&&(c.x=ye(r))),{x:a.left+l.scrollLeft-c.x,y:a.top+l.scrollTop-c.y,width:a.width,height:a.height}}function He(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var Be={placement:"bottom",modifiers:[],strategy:"absolute"};function Re(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function We(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?Be:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},Be,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:$t(t)?Ae(t):t.contextElement?Ae(t.contextElement):[],popper:Ae(e)};var r,c,u=function(t){var e=He(t);return Bt.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(Re(e,i)){a.rects={reference:Me(e,te(i),"fixed"===a.options.strategy),popper:Kt(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!Re(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var $e=We(),ze=We({defaultModifiers:[pe,Pe,ue,Ft]}),qe=We({defaultModifiers:[pe,Pe,ue,Ft,Ie,xe,je,le,Ne]});const Fe=Object.freeze({__proto__:null,popperGenerator:We,detectOverflow:ke,createPopperBase:$e,createPopper:qe,createPopperLite:ze,top:mt,bottom:gt,right:_t,left:bt,auto:vt,basePlacements:yt,start:wt,end:Et,clippingParents:At,viewport:Tt,popper:Ot,reference:Ct,variationPlacements:kt,placements:Lt,beforeRead:xt,read:Dt,afterRead:St,beforeMain:Nt,main:It,afterMain:Pt,beforeWrite:jt,write:Mt,afterWrite:Ht,modifierPhases:Bt,applyStyles:Ft,arrow:le,computeStyles:ue,eventListeners:pe,flip:xe,hide:Ne,offset:Ie,popperOffsets:Pe,preventOverflow:je}),Ue="dropdown",Ve="Escape",Ke="Space",Xe="ArrowUp",Ye="ArrowDown",Qe=new RegExp("ArrowUp|ArrowDown|Escape"),Ge="click.bs.dropdown.data-api",Ze="keydown.bs.dropdown.data-api",Je="show",ti='[data-bs-toggle="dropdown"]',ei=".dropdown-menu",ii=m()?"top-end":"top-start",ni=m()?"top-start":"top-end",si=m()?"bottom-end":"bottom-start",oi=m()?"bottom-start":"bottom-end",ri=m()?"left-start":"right-start",ai=m()?"right-start":"left-start",li={offset:[0,2],boundary:"clippingParents",reference:"toggle",display:"dynamic",popperConfig:null,autoClose:!0},ci={offset:"(array|string|function)",boundary:"(string|element)",reference:"(string|element|object)",display:"string",popperConfig:"(null|object|function)",autoClose:"(boolean|string)"};class hi extends B{constructor(t,e){super(t),this._popper=null,this._config=this._getConfig(e),this._menu=this._getMenuElement(),this._inNavbar=this._detectNavbar()}static get Default(){return li}static get DefaultType(){return ci}static get NAME(){return Ue}toggle(){return this._isShown()?this.hide():this.show()}show(){if(c(this._element)||this._isShown(this._menu))return;const t={relatedTarget:this._element};if(j.trigger(this._element,"show.bs.dropdown",t).defaultPrevented)return;const e=hi.getParentFromElement(this._element);this._inNavbar?U.setDataAttribute(this._menu,"popper","none"):this._createPopper(e),"ontouchstart"in document.documentElement&&!e.closest(".navbar-nav")&&[].concat(...document.body.children).forEach((t=>j.on(t,"mouseover",d))),this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Je),this._element.classList.add(Je),j.trigger(this._element,"shown.bs.dropdown",t)}hide(){if(c(this._element)||!this._isShown(this._menu))return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){j.trigger(this._element,"hide.bs.dropdown",t).defaultPrevented||("ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;i<n;i++){const n=hi.getInstance(e[i]);if(!n||!1===n._config.autoClose)continue;if(!n._isShown())continue;const s={relatedTarget:n._element};if(t){const e=t.composedPath(),i=e.includes(n._menu);if(e.includes(n._element)||"inside"===n._config.autoClose&&!i||"outside"===n._config.autoClose&&i)continue;if(n._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;"click"===t.type&&(s.clickEvent=t)}n._completeHide(s)}}static getParentFromElement(t){return n(t)||t.parentNode}static dataApiKeydownHandler(t){if(/input|textarea/i.test(t.target.tagName)?t.key===Ke||t.key!==Ve&&(t.key!==Ye&&t.key!==Xe||t.target.closest(ei)):!Qe.test(t.key))return;const e=this.classList.contains(Je);if(!e&&t.key===Ve)return;if(t.preventDefault(),t.stopPropagation(),c(this))return;const i=this.matches(ti)?this:V.prev(this,ti)[0],n=hi.getOrCreateInstance(i);if(t.key!==Ve)return t.key===Xe||t.key===Ye?(e||n.show(),void n._selectMenuItem(t)):void(e&&t.key!==Ke||hi.clearMenus());n.hide()}}j.on(document,Ze,ti,hi.dataApiKeydownHandler),j.on(document,Ze,ei,hi.dataApiKeydownHandler),j.on(document,Ge,hi.clearMenus),j.on(document,"keyup.bs.dropdown.data-api",hi.clearMenus),j.on(document,Ge,ti,(function(t){t.preventDefault(),hi.getOrCreateInstance(this).toggle()})),g(hi);const di=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",ui=".sticky-top";class fi{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,"paddingRight",(e=>e+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t<e;t++)if(n[t].test(i))return!0;return!1};function Yi(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(let t=0,i=s.length;t<i;t++){const i=s[t],n=i.nodeName.toLowerCase();if(!Object.keys(e).includes(n)){i.remove();continue}const o=[].concat(...i.attributes),r=[].concat(e["*"]||[],e[n]||[]);o.forEach((t=>{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>'},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const t=this._scrollElement===this._scrollElement.window?"offset":En,e="auto"===this._config.method?t:this._config.method,n=e===En?this._getScrollTop():0;this._offsets=[],this._targets=[],this._scrollHeight=this._getScrollHeight(),V.find(wn,this._config.target).map((t=>{const s=i(t),o=s?V.findOne(s):null;if(o){const t=o.getBoundingClientRect();if(t.width||t.height)return[U[e](o).top+n,s]}return null})).filter((t=>t)).sort(((t,e)=>t[0]-e[0])).forEach((t=>{this._offsets.push(t[0]),this._targets.push(t[1])}))}dispose(){j.off(this._scrollElement,".bs.scrollspy"),super.dispose()}_getConfig(t){return(t={...bn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}}).target=r(t.target)||document.documentElement,a(_n,t,vn),t}_getScrollTop(){return this._scrollElement===window?this._scrollElement.pageYOffset:this._scrollElement.scrollTop}_getScrollHeight(){return this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t<this._offsets[0]&&this._offsets[0]>0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t<this._offsets[e+1])&&this._activate(this._targets[e])}}_activate(t){this._activeTarget=t,this._clear();const e=wn.split(",").map((e=>`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, .list-group").forEach((t=>{V.prev(t,".nav-link, .list-group-item").forEach((t=>t.classList.add(yn))),V.prev(t,".nav-item").forEach((t=>{V.children(t,".nav-link").forEach((t=>t.classList.add(yn)))}))})),j.trigger(this._scrollElement,"activate.bs.scrollspy",{relatedTarget:t})}_clear(){V.find(wn,this._config.target).filter((t=>t.classList.contains(yn))).forEach((t=>t.classList.remove(yn)))}static jQueryInterface(t){return this.each((function(){const e=An.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(window,"load.bs.scrollspy.data-api",(()=>{V.find('[data-bs-spy="scroll"]').forEach((t=>new An(t)))})),g(An);const Tn="active",On="fade",Cn="show",kn=".active",Ln=":scope > li > .active";class xn extends B{static get NAME(){return"tab"}show(){if(this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE&&this._element.classList.contains(Tn))return;let t;const e=n(this._element),i=this._element.closest(".nav, .list-group");if(i){const e="UL"===i.nodeName||"OL"===i.nodeName?Ln:kn;t=V.find(e,i),t=t[t.length-1]}const s=t?j.trigger(t,"hide.bs.tab",{relatedTarget:this._element}):null;if(j.trigger(this._element,"show.bs.tab",{relatedTarget:t}).defaultPrevented||null!==s&&s.defaultPrevented)return;this._activate(this._element,i);const o=()=>{j.trigger(t,"hidden.bs.tab",{relatedTarget:this._element}),j.trigger(this._element,"shown.bs.tab",{relatedTarget:t})};e?this._activate(e,e.parentNode,o):o()}_activate(t,e,i){const n=(!e||"UL"!==e.nodeName&&"OL"!==e.nodeName?V.children(e,kn):V.find(Ln,e))[0],s=i&&n&&n.classList.contains(On),o=()=>this._transitionComplete(t,n,i);n&&s?(n.classList.remove(Cn),this._queueCallback(o,t,!0)):o()}_transitionComplete(t,e,i){if(e){e.classList.remove(Tn);const t=V.findOne(":scope > .dropdown-menu .active",e.parentNode);t&&t.classList.remove(Tn),"tab"===e.getAttribute("role")&&e.setAttribute("aria-selected",!1)}t.classList.add(Tn),"tab"===t.getAttribute("role")&&t.setAttribute("aria-selected",!0),u(t),t.classList.contains(On)&&t.classList.add(Cn);let n=t.parentNode;if(n&&"LI"===n.nodeName&&(n=n.parentNode),n&&n.classList.contains("dropdown-menu")){const e=t.closest(".dropdown");e&&V.find(".dropdown-toggle",e).forEach((t=>t.classList.add(Tn))),t.setAttribute("aria-expanded",!0)}i&&i()}static jQueryInterface(t){return this.each((function(){const e=xn.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}j.on(document,"click.bs.tab.data-api",'[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this)||xn.getOrCreateInstance(this).show()})),g(xn);const Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return Dn}show(){j.trigger(this._element,"show.bs.toast").defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(Sn),u(this._element),this._element.classList.add(Nn),this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.remove(In),j.trigger(this._element,"shown.bs.toast"),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this._element.classList.contains(Nn)&&(j.trigger(this._element,"hide.bs.toast").defaultPrevented||(this._element.classList.add(In),this._queueCallback((()=>{this._element.classList.add(Sn),this._element.classList.remove(In),this._element.classList.remove(Nn),j.trigger(this._element,"hidden.bs.toast")}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this._element.classList.contains(Nn)&&this._element.classList.remove(Nn),super.dispose()}_getConfig(t){return t={...jn,...U.getDataAttributes(this._element),..."object"==typeof t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}}));
+//# sourceMappingURL=bootstrap.bundle.min.js.map
\ No newline at end of file
diff --git a/docs/site_libs/clipboard/clipboard.min.js b/docs/site_libs/clipboard/clipboard.min.js
new file mode 100644
index 00000000..1103f811
--- /dev/null
+++ b/docs/site_libs/clipboard/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/anchor.min.js b/docs/site_libs/quarto-html/anchor.min.js
new file mode 100644
index 00000000..1c2b86fa
--- /dev/null
+++ b/docs/site_libs/quarto-html/anchor.min.js
@@ -0,0 +1,9 @@
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.3.1 - 2021-04-17
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2021 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function d(A){A.icon=Object.prototype.hasOwnProperty.call(A,"icon")?A.icon:"",A.visible=Object.prototype.hasOwnProperty.call(A,"visible")?A.visible:"hover",A.placement=Object.prototype.hasOwnProperty.call(A,"placement")?A.placement:"right",A.ariaLabel=Object.prototype.hasOwnProperty.call(A,"ariaLabel")?A.ariaLabel:"Anchor",A.class=Object.prototype.hasOwnProperty.call(A,"class")?A.class:"",A.base=Object.prototype.hasOwnProperty.call(A,"base")?A.base:"",A.truncate=Object.prototype.hasOwnProperty.call(A,"truncate")?Math.floor(A.truncate):64,A.titleText=Object.prototype.hasOwnProperty.call(A,"titleText")?A.titleText:""}function w(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new TypeError("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],d(this.options),this.isTouchDevice=function(){return Boolean("ontouchstart"in window||window.TouchEvent||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,o,i,n,s,a,c,r,l,h,u,p=[];if(d(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=w(A=A||"h2, h3, h4, h5, h6")).length)return this;for(null===document.head.querySelector("style.anchorjs")&&((u=document.createElement("style")).className="anchorjs",u.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"],style'))?document.head.appendChild(u):document.head.insertBefore(u,A),u.sheet.insertRule(".anchorjs-link{opacity:0;text-decoration:none;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale}",u.sheet.cssRules.length),u.sheet.insertRule(":hover>.anchorjs-link,.anchorjs-link:focus{opacity:1}",u.sheet.cssRules.length),u.sheet.insertRule("[data-anchorjs-icon]::after{content:attr(data-anchorjs-icon)}",u.sheet.cssRules.length),u.sheet.insertRule('@font-face{font-family:anchorjs-icons;src:url(data:n/a;base64,AAEAAAALAIAAAwAwT1MvMg8yG2cAAAE4AAAAYGNtYXDp3gC3AAABpAAAAExnYXNwAAAAEAAAA9wAAAAIZ2x5ZlQCcfwAAAH4AAABCGhlYWQHFvHyAAAAvAAAADZoaGVhBnACFwAAAPQAAAAkaG10eASAADEAAAGYAAAADGxvY2EACACEAAAB8AAAAAhtYXhwAAYAVwAAARgAAAAgbmFtZQGOH9cAAAMAAAAAunBvc3QAAwAAAAADvAAAACAAAQAAAAEAAHzE2p9fDzz1AAkEAAAAAADRecUWAAAAANQA6R8AAAAAAoACwAAAAAgAAgAAAAAAAAABAAADwP/AAAACgAAA/9MCrQABAAAAAAAAAAAAAAAAAAAAAwABAAAAAwBVAAIAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAMCQAGQAAUAAAKZAswAAACPApkCzAAAAesAMwEJAAAAAAAAAAAAAAAAAAAAARAAAAAAAAAAAAAAAAAAAAAAQAAg//0DwP/AAEADwABAAAAAAQAAAAAAAAAAAAAAIAAAAAAAAAIAAAACgAAxAAAAAwAAAAMAAAAcAAEAAwAAABwAAwABAAAAHAAEADAAAAAIAAgAAgAAACDpy//9//8AAAAg6cv//f///+EWNwADAAEAAAAAAAAAAAAAAAAACACEAAEAAAAAAAAAAAAAAAAxAAACAAQARAKAAsAAKwBUAAABIiYnJjQ3NzY2MzIWFxYUBwcGIicmNDc3NjQnJiYjIgYHBwYUFxYUBwYGIwciJicmNDc3NjIXFhQHBwYUFxYWMzI2Nzc2NCcmNDc2MhcWFAcHBgYjARQGDAUtLXoWOR8fORYtLTgKGwoKCjgaGg0gEhIgDXoaGgkJBQwHdR85Fi0tOAobCgoKOBoaDSASEiANehoaCQkKGwotLXoWOR8BMwUFLYEuehYXFxYugC44CQkKGwo4GkoaDQ0NDXoaShoKGwoFBe8XFi6ALjgJCQobCjgaShoNDQ0NehpKGgobCgoKLYEuehYXAAAADACWAAEAAAAAAAEACAAAAAEAAAAAAAIAAwAIAAEAAAAAAAMACAAAAAEAAAAAAAQACAAAAAEAAAAAAAUAAQALAAEAAAAAAAYACAAAAAMAAQQJAAEAEAAMAAMAAQQJAAIABgAcAAMAAQQJAAMAEAAMAAMAAQQJAAQAEAAMAAMAAQQJAAUAAgAiAAMAAQQJAAYAEAAMYW5jaG9yanM0MDBAAGEAbgBjAGgAbwByAGoAcwA0ADAAMABAAAAAAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAH//wAP) format("truetype")}',u.sheet.cssRules.length)),u=document.querySelectorAll("[id]"),t=[].map.call(u,function(A){return A.id}),i=0;i<e.length;i++)if(this.hasAnchorJSLink(e[i]))p.push(i);else{if(e[i].hasAttribute("id"))o=e[i].getAttribute("id");else if(e[i].hasAttribute("data-anchor-id"))o=e[i].getAttribute("data-anchor-id");else{for(c=a=this.urlify(e[i].textContent),s=0;n=t.indexOf(c=void 0!==n?a+"-"+s:c),s+=1,-1!==n;);n=void 0,t.push(c),e[i].setAttribute("id",c),o=c}(r=document.createElement("a")).className="anchorjs-link "+this.options.class,r.setAttribute("aria-label",this.options.ariaLabel),r.setAttribute("data-anchorjs-icon",this.options.icon),this.options.titleText&&(r.title=this.options.titleText),h=document.querySelector("base")?window.location.pathname+window.location.search:"",h=this.options.base||h,r.href=h+"#"+o,"always"===l&&(r.style.opacity="1"),""===this.options.icon&&(r.style.font="1em/1 anchorjs-icons","left"===this.options.placement&&(r.style.lineHeight="inherit")),"left"===this.options.placement?(r.style.position="absolute",r.style.marginLeft="-1em",r.style.paddingRight=".5em",e[i].insertBefore(r,e[i].firstChild)):(r.style.paddingLeft=".375em",e[i].appendChild(r))}for(i=0;i<p.length;i++)e.splice(p[i]-i,1);return this.elements=this.elements.concat(e),this},this.remove=function(A){for(var e,t,o=w(A),i=0;i<o.length;i++)(t=o[i].querySelector(".anchorjs-link"))&&(-1!==(e=this.elements.indexOf(o[i]))&&this.elements.splice(e,1),o[i].removeChild(t));return this},this.removeAll=function(){this.remove(this.elements)},this.urlify=function(A){var e=document.createElement("textarea");return e.innerHTML=A,A=e.value,this.options.truncate||d(this.options),A.trim().replace(/'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]./()*\\\n\t\b\v\u00A0]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),A=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||A||!1}}});
+// @license-end
\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/popper.min.js b/docs/site_libs/quarto-html/popper.min.js
new file mode 100644
index 00000000..2269d669
--- /dev/null
+++ b/docs/site_libs/quarto-html/popper.min.js
@@ -0,0 +1,6 @@
+/**
+ * @popperjs/core v2.11.4 - MIT License
+ */
+
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self).Popper={})}(this,(function(e){"use strict";function t(e){if(null==e)return window;if("[object Window]"!==e.toString()){var t=e.ownerDocument;return t&&t.defaultView||window}return e}function n(e){return e instanceof t(e).Element||e instanceof Element}function r(e){return e instanceof t(e).HTMLElement||e instanceof HTMLElement}function o(e){return"undefined"!=typeof ShadowRoot&&(e instanceof t(e).ShadowRoot||e instanceof ShadowRoot)}var i=Math.max,a=Math.min,s=Math.round;function f(e,t){void 0===t&&(t=!1);var n=e.getBoundingClientRect(),o=1,i=1;if(r(e)&&t){var a=e.offsetHeight,f=e.offsetWidth;f>0&&(o=s(n.width)/f||1),a>0&&(i=s(n.height)/a||1)}return{width:n.width/o,height:n.height/i,top:n.top/i,right:n.right/o,bottom:n.bottom/i,left:n.left/o,x:n.left/o,y:n.top/i}}function c(e){var 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+
diff --git a/docs/site_libs/quarto-html/quarto-syntax-highlighting.css b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
new file mode 100644
index 00000000..d9fd98f0
--- /dev/null
+++ b/docs/site_libs/quarto-html/quarto-syntax-highlighting.css
@@ -0,0 +1,203 @@
+/* quarto syntax highlight colors */
+:root {
+  --quarto-hl-ot-color: #003B4F;
+  --quarto-hl-at-color: #657422;
+  --quarto-hl-ss-color: #20794D;
+  --quarto-hl-an-color: #5E5E5E;
+  --quarto-hl-fu-color: #4758AB;
+  --quarto-hl-st-color: #20794D;
+  --quarto-hl-cf-color: #003B4F;
+  --quarto-hl-op-color: #5E5E5E;
+  --quarto-hl-er-color: #AD0000;
+  --quarto-hl-bn-color: #AD0000;
+  --quarto-hl-al-color: #AD0000;
+  --quarto-hl-va-color: #111111;
+  --quarto-hl-bu-color: inherit;
+  --quarto-hl-ex-color: inherit;
+  --quarto-hl-pp-color: #AD0000;
+  --quarto-hl-in-color: #5E5E5E;
+  --quarto-hl-vs-color: #20794D;
+  --quarto-hl-wa-color: #5E5E5E;
+  --quarto-hl-do-color: #5E5E5E;
+  --quarto-hl-im-color: #00769E;
+  --quarto-hl-ch-color: #20794D;
+  --quarto-hl-dt-color: #AD0000;
+  --quarto-hl-fl-color: #AD0000;
+  --quarto-hl-co-color: #5E5E5E;
+  --quarto-hl-cv-color: #5E5E5E;
+  --quarto-hl-cn-color: #8f5902;
+  --quarto-hl-sc-color: #5E5E5E;
+  --quarto-hl-dv-color: #AD0000;
+  --quarto-hl-kw-color: #003B4F;
+}
+
+/* other quarto variables */
+:root {
+  --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+pre > code.sourceCode > span {
+  color: #003B4F;
+}
+
+code span {
+  color: #003B4F;
+}
+
+code.sourceCode > span {
+  color: #003B4F;
+}
+
+div.sourceCode,
+div.sourceCode pre.sourceCode {
+  color: #003B4F;
+}
+
+code span.ot {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+code span.at {
+  color: #657422;
+  font-style: inherit;
+}
+
+code span.ss {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.an {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.fu {
+  color: #4758AB;
+  font-style: inherit;
+}
+
+code span.st {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.cf {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+code span.op {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.er {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.bn {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.al {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.va {
+  color: #111111;
+  font-style: inherit;
+}
+
+code span.bu {
+  font-style: inherit;
+}
+
+code span.ex {
+  font-style: inherit;
+}
+
+code span.pp {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.in {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.vs {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.wa {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.do {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.im {
+  color: #00769E;
+  font-style: inherit;
+}
+
+code span.ch {
+  color: #20794D;
+  font-style: inherit;
+}
+
+code span.dt {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.fl {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.co {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.cv {
+  color: #5E5E5E;
+  font-style: italic;
+}
+
+code span.cn {
+  color: #8f5902;
+  font-style: inherit;
+}
+
+code span.sc {
+  color: #5E5E5E;
+  font-style: inherit;
+}
+
+code span.dv {
+  color: #AD0000;
+  font-style: inherit;
+}
+
+code span.kw {
+  color: #003B4F;
+  font-style: inherit;
+}
+
+.prevent-inlining {
+  content: "</";
+}
+
+/*# sourceMappingURL=debc5d5d77c3f9108843748ff7464032.css.map */
diff --git a/docs/site_libs/quarto-html/quarto.js b/docs/site_libs/quarto-html/quarto.js
new file mode 100644
index 00000000..deb7dff0
--- /dev/null
+++ b/docs/site_libs/quarto-html/quarto.js
@@ -0,0 +1,884 @@
+const sectionChanged = new CustomEvent("quarto-sectionChanged", {
+  detail: {},
+  bubbles: true,
+  cancelable: false,
+  composed: false,
+});
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]');
+  const sidebarEl = window.document.getElementById("quarto-sidebar");
+  const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left");
+  const marginSidebarEl = window.document.getElementById(
+    "quarto-margin-sidebar"
+  );
+  // function to determine whether the element has a previous sibling that is active
+  const prevSiblingIsActiveLink = (el) => {
+    const sibling = el.previousElementSibling;
+    if (sibling && sibling.tagName === "A") {
+      return sibling.classList.contains("active");
+    } else {
+      return false;
+    }
+  };
+
+  // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior)
+  function fireSlideEnter(e) {
+    const event = window.document.createEvent("Event");
+    event.initEvent("slideenter", true, true);
+    window.document.dispatchEvent(event);
+  }
+  const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]');
+  tabs.forEach((tab) => {
+    tab.addEventListener("shown.bs.tab", fireSlideEnter);
+  });
+
+  // fire slideEnter for tabby tab activations (for htmlwidget resize behavior)
+  document.addEventListener("tabby", fireSlideEnter, false);
+
+  // Track scrolling and mark TOC links as active
+  // get table of contents and sidebar (bail if we don't have at least one)
+  const tocLinks = tocEl
+    ? [...tocEl.querySelectorAll("a[data-scroll-target]")]
+    : [];
+  const makeActive = (link) => tocLinks[link].classList.add("active");
+  const removeActive = (link) => tocLinks[link].classList.remove("active");
+  const removeAllActive = () =>
+    [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link));
+
+  // activate the anchor for a section associated with this TOC entry
+  tocLinks.forEach((link) => {
+    link.addEventListener("click", () => {
+      if (link.href.indexOf("#") !== -1) {
+        const anchor = link.href.split("#")[1];
+        const heading = window.document.querySelector(
+          `[data-anchor-id=${anchor}]`
+        );
+        if (heading) {
+          // Add the class
+          heading.classList.add("reveal-anchorjs-link");
+
+          // function to show the anchor
+          const handleMouseout = () => {
+            heading.classList.remove("reveal-anchorjs-link");
+            heading.removeEventListener("mouseout", handleMouseout);
+          };
+
+          // add a function to clear the anchor when the user mouses out of it
+          heading.addEventListener("mouseout", handleMouseout);
+        }
+      }
+    });
+  });
+
+  const sections = tocLinks.map((link) => {
+    const target = link.getAttribute("data-scroll-target");
+    if (target.startsWith("#")) {
+      return window.document.getElementById(decodeURI(`${target.slice(1)}`));
+    } else {
+      return window.document.querySelector(decodeURI(`${target}`));
+    }
+  });
+
+  const sectionMargin = 200;
+  let currentActive = 0;
+  // track whether we've initialized state the first time
+  let init = false;
+
+  const updateActiveLink = () => {
+    // The index from bottom to top (e.g. reversed list)
+    let sectionIndex = -1;
+    if (
+      window.innerHeight + window.pageYOffset >=
+      window.document.body.offsetHeight
+    ) {
+      sectionIndex = 0;
+    } else {
+      sectionIndex = [...sections].reverse().findIndex((section) => {
+        if (section) {
+          return window.pageYOffset >= section.offsetTop - sectionMargin;
+        } else {
+          return false;
+        }
+      });
+    }
+    if (sectionIndex > -1) {
+      const current = sections.length - sectionIndex - 1;
+      if (current !== currentActive) {
+        removeAllActive();
+        currentActive = current;
+        makeActive(current);
+        if (init) {
+          window.dispatchEvent(sectionChanged);
+        }
+        init = true;
+      }
+    }
+  };
+
+  const inHiddenRegion = (top, bottom, hiddenRegions) => {
+    for (const region of hiddenRegions) {
+      if (top <= region.bottom && bottom >= region.top) {
+        return true;
+      }
+    }
+    return false;
+  };
+
+  const categorySelector = "header.quarto-title-block .quarto-category";
+  const activateCategories = (href) => {
+    // Find any categories
+    // Surround them with a link pointing back to:
+    // #category=Authoring
+    try {
+      const categoryEls = window.document.querySelectorAll(categorySelector);
+      for (const categoryEl of categoryEls) {
+        const categoryText = categoryEl.textContent;
+        if (categoryText) {
+          const link = `${href}#category=${encodeURIComponent(categoryText)}`;
+          const linkEl = window.document.createElement("a");
+          linkEl.setAttribute("href", link);
+          for (const child of categoryEl.childNodes) {
+            linkEl.append(child);
+          }
+          categoryEl.appendChild(linkEl);
+        }
+      }
+    } catch {
+      // Ignore errors
+    }
+  };
+  function hasTitleCategories() {
+    return window.document.querySelector(categorySelector) !== null;
+  }
+
+  function offsetRelativeUrl(url) {
+    const offset = getMeta("quarto:offset");
+    return offset ? offset + url : url;
+  }
+
+  function offsetAbsoluteUrl(url) {
+    const offset = getMeta("quarto:offset");
+    const baseUrl = new URL(offset, window.location);
+
+    const projRelativeUrl = url.replace(baseUrl, "");
+    if (projRelativeUrl.startsWith("/")) {
+      return projRelativeUrl;
+    } else {
+      return "/" + projRelativeUrl;
+    }
+  }
+
+  // read a meta tag value
+  function getMeta(metaName) {
+    const metas = window.document.getElementsByTagName("meta");
+    for (let i = 0; i < metas.length; i++) {
+      if (metas[i].getAttribute("name") === metaName) {
+        return metas[i].getAttribute("content");
+      }
+    }
+    return "";
+  }
+
+  async function findAndActivateCategories() {
+    const currentPagePath = offsetAbsoluteUrl(window.location.href);
+    const response = await fetch(offsetRelativeUrl("listings.json"));
+    if (response.status == 200) {
+      return response.json().then(function (listingPaths) {
+        const listingHrefs = [];
+        for (const listingPath of listingPaths) {
+          const pathWithoutLeadingSlash = listingPath.listing.substring(1);
+          for (const item of listingPath.items) {
+            if (
+              item === currentPagePath ||
+              item === currentPagePath + "index.html"
+            ) {
+              // Resolve this path against the offset to be sure
+              // we already are using the correct path to the listing
+              // (this adjusts the listing urls to be rooted against
+              // whatever root the page is actually running against)
+              const relative = offsetRelativeUrl(pathWithoutLeadingSlash);
+              const baseUrl = window.location;
+              const resolvedPath = new URL(relative, baseUrl);
+              listingHrefs.push(resolvedPath.pathname);
+              break;
+            }
+          }
+        }
+
+        // Look up the tree for a nearby linting and use that if we find one
+        const nearestListing = findNearestParentListing(
+          offsetAbsoluteUrl(window.location.pathname),
+          listingHrefs
+        );
+        if (nearestListing) {
+          activateCategories(nearestListing);
+        } else {
+          // See if the referrer is a listing page for this item
+          const referredRelativePath = offsetAbsoluteUrl(document.referrer);
+          const referrerListing = listingHrefs.find((listingHref) => {
+            const isListingReferrer =
+              listingHref === referredRelativePath ||
+              listingHref === referredRelativePath + "index.html";
+            return isListingReferrer;
+          });
+
+          if (referrerListing) {
+            // Try to use the referrer if possible
+            activateCategories(referrerListing);
+          } else if (listingHrefs.length > 0) {
+            // Otherwise, just fall back to the first listing
+            activateCategories(listingHrefs[0]);
+          }
+        }
+      });
+    }
+  }
+  if (hasTitleCategories()) {
+    findAndActivateCategories();
+  }
+
+  const findNearestParentListing = (href, listingHrefs) => {
+    if (!href || !listingHrefs) {
+      return undefined;
+    }
+    // Look up the tree for a nearby linting and use that if we find one
+    const relativeParts = href.substring(1).split("/");
+    while (relativeParts.length > 0) {
+      const path = relativeParts.join("/");
+      for (const listingHref of listingHrefs) {
+        if (listingHref.startsWith(path)) {
+          return listingHref;
+        }
+      }
+      relativeParts.pop();
+    }
+
+    return undefined;
+  };
+
+  const manageSidebarVisiblity = (el, placeholderDescriptor) => {
+    let isVisible = true;
+    let elRect;
+
+    return (hiddenRegions) => {
+      if (el === null) {
+        return;
+      }
+
+      // Find the last element of the TOC
+      const lastChildEl = el.lastElementChild;
+
+      if (lastChildEl) {
+        // Converts the sidebar to a menu
+        const convertToMenu = () => {
+          for (const child of el.children) {
+            child.style.opacity = 0;
+            child.style.overflow = "hidden";
+          }
+
+          nexttick(() => {
+            const toggleContainer = window.document.createElement("div");
+            toggleContainer.style.width = "100%";
+            toggleContainer.classList.add("zindex-over-content");
+            toggleContainer.classList.add("quarto-sidebar-toggle");
+            toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom
+            toggleContainer.id = placeholderDescriptor.id;
+            toggleContainer.style.position = "fixed";
+
+            const toggleIcon = window.document.createElement("i");
+            toggleIcon.classList.add("quarto-sidebar-toggle-icon");
+            toggleIcon.classList.add("bi");
+            toggleIcon.classList.add("bi-caret-down-fill");
+
+            const toggleTitle = window.document.createElement("div");
+            const titleEl = window.document.body.querySelector(
+              placeholderDescriptor.titleSelector
+            );
+            if (titleEl) {
+              toggleTitle.append(
+                titleEl.textContent || titleEl.innerText,
+                toggleIcon
+              );
+            }
+            toggleTitle.classList.add("zindex-over-content");
+            toggleTitle.classList.add("quarto-sidebar-toggle-title");
+            toggleContainer.append(toggleTitle);
+
+            const toggleContents = window.document.createElement("div");
+            toggleContents.classList = el.classList;
+            toggleContents.classList.add("zindex-over-content");
+            toggleContents.classList.add("quarto-sidebar-toggle-contents");
+            for (const child of el.children) {
+              if (child.id === "toc-title") {
+                continue;
+              }
+
+              const clone = child.cloneNode(true);
+              clone.style.opacity = 1;
+              clone.style.display = null;
+              toggleContents.append(clone);
+            }
+            toggleContents.style.height = "0px";
+            const positionToggle = () => {
+              // position the element (top left of parent, same width as parent)
+              if (!elRect) {
+                elRect = el.getBoundingClientRect();
+              }
+              toggleContainer.style.left = `${elRect.left}px`;
+              toggleContainer.style.top = `${elRect.top}px`;
+              toggleContainer.style.width = `${elRect.width}px`;
+            };
+            positionToggle();
+
+            toggleContainer.append(toggleContents);
+            el.parentElement.prepend(toggleContainer);
+
+            // Process clicks
+            let tocShowing = false;
+            // Allow the caller to control whether this is dismissed
+            // when it is clicked (e.g. sidebar navigation supports
+            // opening and closing the nav tree, so don't dismiss on click)
+            const clickEl = placeholderDescriptor.dismissOnClick
+              ? toggleContainer
+              : toggleTitle;
+
+            const closeToggle = () => {
+              if (tocShowing) {
+                toggleContainer.classList.remove("expanded");
+                toggleContents.style.height = "0px";
+                tocShowing = false;
+              }
+            };
+
+            // Get rid of any expanded toggle if the user scrolls
+            window.document.addEventListener(
+              "scroll",
+              throttle(() => {
+                closeToggle();
+              }, 50)
+            );
+
+            // Handle positioning of the toggle
+            window.addEventListener(
+              "resize",
+              throttle(() => {
+                elRect = undefined;
+                positionToggle();
+              }, 50)
+            );
+
+            window.addEventListener("quarto-hrChanged", () => {
+              elRect = undefined;
+            });
+
+            // Process the click
+            clickEl.onclick = () => {
+              if (!tocShowing) {
+                toggleContainer.classList.add("expanded");
+                toggleContents.style.height = null;
+                tocShowing = true;
+              } else {
+                closeToggle();
+              }
+            };
+          });
+        };
+
+        // Converts a sidebar from a menu back to a sidebar
+        const convertToSidebar = () => {
+          for (const child of el.children) {
+            child.style.opacity = 1;
+            child.style.overflow = null;
+          }
+
+          const placeholderEl = window.document.getElementById(
+            placeholderDescriptor.id
+          );
+          if (placeholderEl) {
+            placeholderEl.remove();
+          }
+
+          el.classList.remove("rollup");
+        };
+
+        if (isReaderMode()) {
+          convertToMenu();
+          isVisible = false;
+        } else {
+          // Find the top and bottom o the element that is being managed
+          const elTop = el.offsetTop;
+          const elBottom =
+            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;
+
+          if (!isVisible) {
+            // If the element is current not visible reveal if there are
+            // no conflicts with overlay regions
+            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToSidebar();
+              isVisible = true;
+            }
+          } else {
+            // If the element is visible, hide it if it conflicts with overlay regions
+            // and insert a placeholder toggle (or if we're in reader mode)
+            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
+              convertToMenu();
+              isVisible = false;
+            }
+          }
+        }
+      }
+    };
+  };
+
+  // Find any conflicting margin elements and add margins to the
+  // top to prevent overlap
+  const marginChildren = window.document.querySelectorAll(
+    ".column-margin.column-container > * "
+  );
+
+  const layoutMarginEls = () => {
+    let lastBottom = 0;
+    for (const marginChild of marginChildren) {
+      if (marginChild.offsetParent !== null) {
+        // clear the top margin so we recompute it
+        marginChild.style.marginTop = null;
+        const top = marginChild.getBoundingClientRect().top + window.scrollY;
+        if (top < lastBottom) {
+          const margin = lastBottom - top;
+          marginChild.style.marginTop = `${margin}px`;
+        }
+        const styles = window.getComputedStyle(marginChild);
+        const marginTop = parseFloat(styles["marginTop"]);
+        lastBottom =
+          top + marginChild.getBoundingClientRect().height + marginTop;
+      }
+    }
+  };
+  nexttick(layoutMarginEls);
+
+  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
+  for (const tabEl of tabEls) {
+    const id = tabEl.getAttribute("data-bs-target");
+    if (id) {
+      const columnEl = document.querySelector(
+        `${id} .column-margin, .tabset-margin-content`
+      );
+      if (columnEl)
+        tabEl.addEventListener("shown.bs.tab", function (event) {
+
+          const el = event.srcElement;
+          if (el) {
+            const visibleCls = `${el.id}-margin-content`;
+            // walk up until we find a parent tabset
+            let panelTabsetEl = el.parentElement;
+            while (panelTabsetEl) {
+              if (panelTabsetEl.classList.contains("panel-tabset")) {
+                break;
+              }
+              panelTabsetEl = panelTabsetEl.parentElement;
+            }
+
+            if (panelTabsetEl) {
+              const prevSib = panelTabsetEl.previousElementSibling;
+              if (
+                prevSib &&
+                prevSib.classList.contains("tabset-margin-container")
+              ) {
+                const childNodes = prevSib.querySelectorAll(
+                  ".tabset-margin-content"
+                );
+                for (const childEl of childNodes) {
+                  if (childEl.classList.contains(visibleCls)) {
+                    childEl.classList.remove("collapse");
+                  } else {
+                    childEl.classList.add("collapse");
+                  }
+                }
+              }
+            }
+          }
+
+          layoutMarginEls();
+        });
+    }
+  }
+
+  // Manage the visibility of the toc and the sidebar
+  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
+    id: "quarto-toc-toggle",
+    titleSelector: "#toc-title",
+    dismissOnClick: true,
+  });
+  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
+    id: "quarto-sidebarnav-toggle",
+    titleSelector: ".title",
+    dismissOnClick: false,
+  });
+  let tocLeftScrollVisibility;
+  if (leftTocEl) {
+    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
+      id: "quarto-lefttoc-toggle",
+      titleSelector: "#toc-title",
+      dismissOnClick: true,
+    });
+  }
+
+  // Find the first element that uses formatting in special columns
+  const conflictingEls = window.document.body.querySelectorAll(
+    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
+  );
+
+  // Filter all the possibly conflicting elements into ones
+  // the do conflict on the left or ride side
+  const arrConflictingEls = Array.from(conflictingEls);
+  const leftSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return false;
+    }
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        className.startsWith("column-") &&
+        !className.endsWith("right") &&
+        !className.endsWith("container") &&
+        className !== "column-margin"
+      );
+    });
+  });
+  const rightSideConflictEls = arrConflictingEls.filter((el) => {
+    if (el.tagName === "ASIDE") {
+      return true;
+    }
+
+    const hasMarginCaption = Array.from(el.classList).find((className) => {
+      return className == "margin-caption";
+    });
+    if (hasMarginCaption) {
+      return true;
+    }
+
+    return Array.from(el.classList).find((className) => {
+      return (
+        className !== "column-body" &&
+        !className.endsWith("container") &&
+        className.startsWith("column-") &&
+        !className.endsWith("left")
+      );
+    });
+  });
+
+  const kOverlapPaddingSize = 10;
+  function toRegions(els) {
+    return els.map((el) => {
+      const boundRect = el.getBoundingClientRect();
+      const top =
+        boundRect.top +
+        document.documentElement.scrollTop -
+        kOverlapPaddingSize;
+      return {
+        top,
+        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
+      };
+    });
+  }
+
+  let hasObserved = false;
+  const visibleItemObserver = (els) => {
+    let visibleElements = [...els];
+    const intersectionObserver = new IntersectionObserver(
+      (entries, _observer) => {
+        entries.forEach((entry) => {
+          if (entry.isIntersecting) {
+            if (visibleElements.indexOf(entry.target) === -1) {
+              visibleElements.push(entry.target);
+            }
+          } else {
+            visibleElements = visibleElements.filter((visibleEntry) => {
+              return visibleEntry !== entry;
+            });
+          }
+        });
+
+        if (!hasObserved) {
+          hideOverlappedSidebars();
+        }
+        hasObserved = true;
+      },
+      {}
+    );
+    els.forEach((el) => {
+      intersectionObserver.observe(el);
+    });
+
+    return {
+      getVisibleEntries: () => {
+        return visibleElements;
+      },
+    };
+  };
+
+  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
+  const leftElementObserver = visibleItemObserver(leftSideConflictEls);
+
+  const hideOverlappedSidebars = () => {
+    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
+    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
+    if (tocLeftScrollVisibility) {
+      tocLeftScrollVisibility(
+        toRegions(leftElementObserver.getVisibleEntries())
+      );
+    }
+  };
+
+  window.quartoToggleReader = () => {
+    // Applies a slow class (or removes it)
+    // to update the transition speed
+    const slowTransition = (slow) => {
+      const manageTransition = (id, slow) => {
+        const el = document.getElementById(id);
+        if (el) {
+          if (slow) {
+            el.classList.add("slow");
+          } else {
+            el.classList.remove("slow");
+          }
+        }
+      };
+
+      manageTransition("TOC", slow);
+      manageTransition("quarto-sidebar", slow);
+    };
+    const readerMode = !isReaderMode();
+    setReaderModeValue(readerMode);
+
+    // If we're entering reader mode, slow the transition
+    if (readerMode) {
+      slowTransition(readerMode);
+    }
+    highlightReaderToggle(readerMode);
+    hideOverlappedSidebars();
+
+    // If we're exiting reader mode, restore the non-slow transition
+    if (!readerMode) {
+      slowTransition(!readerMode);
+    }
+  };
+
+  const highlightReaderToggle = (readerMode) => {
+    const els = document.querySelectorAll(".quarto-reader-toggle");
+    if (els) {
+      els.forEach((el) => {
+        if (readerMode) {
+          el.classList.add("reader");
+        } else {
+          el.classList.remove("reader");
+        }
+      });
+    }
+  };
+
+  const setReaderModeValue = (val) => {
+    if (window.location.protocol !== "file:") {
+      window.localStorage.setItem("quarto-reader-mode", val);
+    } else {
+      localReaderMode = val;
+    }
+  };
+
+  const isReaderMode = () => {
+    if (window.location.protocol !== "file:") {
+      return window.localStorage.getItem("quarto-reader-mode") === "true";
+    } else {
+      return localReaderMode;
+    }
+  };
+  let localReaderMode = null;
+
+  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
+  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;
+
+  // Walk the TOC and collapse/expand nodes
+  // Nodes are expanded if:
+  // - they are top level
+  // - they have children that are 'active' links
+  // - they are directly below an link that is 'active'
+  const walk = (el, depth) => {
+    // Tick depth when we enter a UL
+    if (el.tagName === "UL") {
+      depth = depth + 1;
+    }
+
+    // It this is active link
+    let isActiveNode = false;
+    if (el.tagName === "A" && el.classList.contains("active")) {
+      isActiveNode = true;
+    }
+
+    // See if there is an active child to this element
+    let hasActiveChild = false;
+    for (child of el.children) {
+      hasActiveChild = walk(child, depth) || hasActiveChild;
+    }
+
+    // Process the collapse state if this is an UL
+    if (el.tagName === "UL") {
+      if (tocOpenDepth === -1 && depth > 1) {
+        el.classList.add("collapse");
+      } else if (
+        depth <= tocOpenDepth ||
+        hasActiveChild ||
+        prevSiblingIsActiveLink(el)
+      ) {
+        el.classList.remove("collapse");
+      } else {
+        el.classList.add("collapse");
+      }
+
+      // untick depth when we leave a UL
+      depth = depth - 1;
+    }
+    return hasActiveChild || isActiveNode;
+  };
+
+  // walk the TOC and expand / collapse any items that should be shown
+
+  if (tocEl) {
+    walk(tocEl, 0);
+    updateActiveLink();
+  }
+
+  // Throttle the scroll event and walk peridiocally
+  window.document.addEventListener(
+    "scroll",
+    throttle(() => {
+      if (tocEl) {
+        updateActiveLink();
+        walk(tocEl, 0);
+      }
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 5)
+  );
+  window.addEventListener(
+    "resize",
+    throttle(() => {
+      if (!isReaderMode()) {
+        hideOverlappedSidebars();
+      }
+    }, 10)
+  );
+  hideOverlappedSidebars();
+  highlightReaderToggle(isReaderMode());
+});
+
+// grouped tabsets
+window.addEventListener("pageshow", (_event) => {
+  function getTabSettings() {
+    const data = localStorage.getItem("quarto-persistent-tabsets-data");
+    if (!data) {
+      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
+      return {};
+    }
+    if (data) {
+      return JSON.parse(data);
+    }
+  }
+
+  function setTabSettings(data) {
+    localStorage.setItem(
+      "quarto-persistent-tabsets-data",
+      JSON.stringify(data)
+    );
+  }
+
+  function setTabState(groupName, groupValue) {
+    const data = getTabSettings();
+    data[groupName] = groupValue;
+    setTabSettings(data);
+  }
+
+  function toggleTab(tab, active) {
+    const tabPanelId = tab.getAttribute("aria-controls");
+    const tabPanel = document.getElementById(tabPanelId);
+    if (active) {
+      tab.classList.add("active");
+      tabPanel.classList.add("active");
+    } else {
+      tab.classList.remove("active");
+      tabPanel.classList.remove("active");
+    }
+  }
+
+  function toggleAll(selectedGroup, selectorsToSync) {
+    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
+      const active = selectedGroup === thisGroup;
+      for (const tab of tabs) {
+        toggleTab(tab, active);
+      }
+    }
+  }
+
+  function findSelectorsToSyncByLanguage() {
+    const result = {};
+    const tabs = Array.from(
+      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
+    );
+    for (const item of tabs) {
+      const div = item.parentElement.parentElement.parentElement;
+      const group = div.getAttribute("data-group");
+      if (!result[group]) {
+        result[group] = {};
+      }
+      const selectorsToSync = result[group];
+      const value = item.innerHTML;
+      if (!selectorsToSync[value]) {
+        selectorsToSync[value] = [];
+      }
+      selectorsToSync[value].push(item);
+    }
+    return result;
+  }
+
+  function setupSelectorSync() {
+    const selectorsToSync = findSelectorsToSyncByLanguage();
+    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
+      Object.entries(tabSetsByValue).forEach(([value, items]) => {
+        items.forEach((item) => {
+          item.addEventListener("click", (_event) => {
+            setTabState(group, value);
+            toggleAll(value, selectorsToSync[group]);
+          });
+        });
+      });
+    });
+    return selectorsToSync;
+  }
+
+  const selectorsToSync = setupSelectorSync();
+  for (const [group, selectedName] of Object.entries(getTabSettings())) {
+    const selectors = selectorsToSync[group];
+    // it's possible that stale state gives us empty selections, so we explicitly check here.
+    if (selectors) {
+      toggleAll(selectedName, selectors);
+    }
+  }
+});
+
+function throttle(func, wait) {
+  let waiting = false;
+  return function () {
+    if (!waiting) {
+      func.apply(this, arguments);
+      waiting = true;
+      setTimeout(function () {
+        waiting = false;
+      }, wait);
+    }
+  };
+}
+
+function nexttick(func) {
+  return setTimeout(func, 0);
+}
diff --git a/docs/site_libs/quarto-html/tippy.css b/docs/site_libs/quarto-html/tippy.css
new file mode 100644
index 00000000..e6ae635c
--- /dev/null
+++ b/docs/site_libs/quarto-html/tippy.css
@@ -0,0 +1 @@
+.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
\ No newline at end of file
diff --git a/docs/site_libs/quarto-html/tippy.umd.min.js b/docs/site_libs/quarto-html/tippy.umd.min.js
new file mode 100644
index 00000000..ca292be3
--- /dev/null
+++ b/docs/site_libs/quarto-html/tippy.umd.min.js
@@ -0,0 +1,2 @@
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t(require("@popperjs/core")):"function"==typeof define&&define.amd?define(["@popperjs/core"],t):(e=e||self).tippy=t(e.Popper)}(this,(function(e){"use strict";var t={passive:!0,capture:!0},n=function(){return document.body};function r(e,t,n){if(Array.isArray(e)){var r=e[t];return null==r?Array.isArray(n)?n[t]:n:r}return e}function o(e,t){var n={}.toString.call(e);return 0===n.indexOf("[object")&&n.indexOf(t+"]")>-1}function i(e,t){return"function"==typeof e?e.apply(void 0,t):e}function a(e,t){return 0===t?e:function(r){clearTimeout(n),n=setTimeout((function(){e(r)}),t)};var n}function s(e,t){var n=Object.assign({},e);return t.forEach((function(e){delete n[e]})),n}function u(e){return[].concat(e)}function c(e,t){-1===e.indexOf(t)&&e.push(t)}function p(e){return e.split("-")[0]}function f(e){return[].slice.call(e)}function l(e){return Object.keys(e).reduce((function(t,n){return void 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diff --git a/docs/site_libs/quarto-nav/headroom.min.js b/docs/site_libs/quarto-nav/headroom.min.js
new file mode 100644
index 00000000..b08f1dff
--- /dev/null
+++ b/docs/site_libs/quarto-nav/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.12.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset[a.direction],a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t){return t===Object(t)?t:{down:t,up:t}}function s(t,n){n=n||{},Object.assign(this,s.options,n),this.classes=Object.assign({},s.options.classes,n.classes),this.elem=t,this.tolerance=o(this.tolerance),this.offset=o(this.offset),this.initialised=!1,this.frozen=!1}return s.prototype={constructor:s,init:function(){return s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s});
diff --git a/docs/site_libs/quarto-nav/quarto-nav.js b/docs/site_libs/quarto-nav/quarto-nav.js
new file mode 100644
index 00000000..c83857ec
--- /dev/null
+++ b/docs/site_libs/quarto-nav/quarto-nav.js
@@ -0,0 +1,273 @@
+const headroomChanged = new CustomEvent("quarto-hrChanged", {
+  detail: {},
+  bubbles: true,
+  cancelable: false,
+  composed: false,
+});
+
+window.document.addEventListener("DOMContentLoaded", function () {
+  let init = false;
+
+  // Manage the back to top button, if one is present.
+  let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;
+  const scrollDownBuffer = 5;
+  const scrollUpBuffer = 35;
+  const btn = document.getElementById("quarto-back-to-top");
+  const hideBackToTop = () => {
+    btn.style.display = "none";
+  };
+  const showBackToTop = () => {
+    btn.style.display = "inline-block";
+  };
+  if (btn) {
+    window.document.addEventListener(
+      "scroll",
+      function () {
+        const currentScrollTop =
+          window.pageYOffset || document.documentElement.scrollTop;
+
+        // Shows and hides the button 'intelligently' as the user scrolls
+        if (currentScrollTop - scrollDownBuffer > lastScrollTop) {
+          hideBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) {
+          showBackToTop();
+          lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop;
+        }
+
+        // Show the button at the bottom, hides it at the top
+        if (currentScrollTop <= 0) {
+          hideBackToTop();
+        } else if (
+          window.innerHeight + currentScrollTop >=
+          document.body.offsetHeight
+        ) {
+          showBackToTop();
+        }
+      },
+      false
+    );
+  }
+
+  function throttle(func, wait) {
+    var timeout;
+    return function () {
+      const context = this;
+      const args = arguments;
+      const later = function () {
+        clearTimeout(timeout);
+        timeout = null;
+        func.apply(context, args);
+      };
+
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+      }
+    };
+  }
+
+  function headerOffset() {
+    // Set an offset if there is are fixed top navbar
+    const headerEl = window.document.querySelector("header.fixed-top");
+    if (headerEl) {
+      return headerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function footerOffset() {
+    const footerEl = window.document.querySelector("footer.footer");
+    if (footerEl) {
+      return footerEl.clientHeight;
+    } else {
+      return 0;
+    }
+  }
+
+  function updateDocumentOffsetWithoutAnimation() {
+    updateDocumentOffset(false);
+  }
+
+  function updateDocumentOffset(animated) {
+    // set body offset
+    const topOffset = headerOffset();
+    const bodyOffset = topOffset + footerOffset();
+    const bodyEl = window.document.body;
+    bodyEl.setAttribute("data-bs-offset", topOffset);
+    bodyEl.style.paddingTop = topOffset + "px";
+
+    // deal with sidebar offsets
+    const sidebars = window.document.querySelectorAll(
+      ".sidebar, .headroom-target"
+    );
+    sidebars.forEach((sidebar) => {
+      if (!animated) {
+        sidebar.classList.add("notransition");
+        // Remove the no transition class after the animation has time to complete
+        setTimeout(function () {
+          sidebar.classList.remove("notransition");
+        }, 201);
+      }
+
+      if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) {
+        sidebar.style.top = "0";
+        sidebar.style.maxHeight = "100vh";
+      } else {
+        sidebar.style.top = topOffset + "px";
+        sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)";
+      }
+    });
+
+    // allow space for footer
+    const mainContainer = window.document.querySelector(".quarto-container");
+    if (mainContainer) {
+      mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)";
+    }
+
+    // link offset
+    let linkStyle = window.document.querySelector("#quarto-target-style");
+    if (!linkStyle) {
+      linkStyle = window.document.createElement("style");
+      linkStyle.setAttribute("id", "quarto-target-style");
+      window.document.head.appendChild(linkStyle);
+    }
+    while (linkStyle.firstChild) {
+      linkStyle.removeChild(linkStyle.firstChild);
+    }
+    if (topOffset > 0) {
+      linkStyle.appendChild(
+        window.document.createTextNode(`
+      section:target::before {
+        content: "";
+        display: block;
+        height: ${topOffset}px;
+        margin: -${topOffset}px 0 0;
+      }`)
+      );
+    }
+    if (init) {
+      window.dispatchEvent(headroomChanged);
+    }
+    init = true;
+  }
+
+  // initialize headroom
+  var header = window.document.querySelector("#quarto-header");
+  if (header && window.Headroom) {
+    const headroom = new window.Headroom(header, {
+      tolerance: 5,
+      onPin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.remove("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+      onUnpin: function () {
+        const sidebars = window.document.querySelectorAll(
+          ".sidebar, .headroom-target"
+        );
+        sidebars.forEach((sidebar) => {
+          sidebar.classList.add("sidebar-unpinned");
+        });
+        updateDocumentOffset();
+      },
+    });
+    headroom.init();
+
+    let frozen = false;
+    window.quartoToggleHeadroom = function () {
+      if (frozen) {
+        headroom.unfreeze();
+        frozen = false;
+      } else {
+        headroom.freeze();
+        frozen = true;
+      }
+    };
+  }
+
+  window.addEventListener(
+    "hashchange",
+    function (e) {
+      window.scrollTo(0, window.pageYOffset - headerOffset());
+    },
+    false
+  );
+
+  // Observe size changed for the header
+  const headerEl = window.document.querySelector("header.fixed-top");
+  if (headerEl && window.ResizeObserver) {
+    const observer = new window.ResizeObserver(
+      updateDocumentOffsetWithoutAnimation
+    );
+    observer.observe(headerEl, {
+      attributes: true,
+      childList: true,
+      characterData: true,
+    });
+  } else {
+    window.addEventListener(
+      "resize",
+      throttle(updateDocumentOffsetWithoutAnimation, 50)
+    );
+  }
+  setTimeout(updateDocumentOffsetWithoutAnimation, 250);
+
+  // fixup index.html links if we aren't on the filesystem
+  if (window.location.protocol !== "file:") {
+    const links = window.document.querySelectorAll("a");
+    for (let i = 0; i < links.length; i++) {
+      if (links[i].href) {
+        links[i].href = links[i].href.replace(/\/index\.html/, "/");
+      }
+    }
+
+    // Fixup any sharing links that require urls
+    // Append url to any sharing urls
+    const sharingLinks = window.document.querySelectorAll(
+      "a.sidebar-tools-main-item"
+    );
+    for (let i = 0; i < sharingLinks.length; i++) {
+      const sharingLink = sharingLinks[i];
+      const href = sharingLink.getAttribute("href");
+      if (href) {
+        sharingLink.setAttribute(
+          "href",
+          href.replace("|url|", window.location.href)
+        );
+      }
+    }
+
+    // Scroll the active navigation item into view, if necessary
+    const navSidebar = window.document.querySelector("nav#quarto-sidebar");
+    if (navSidebar) {
+      // Find the active item
+      const activeItem = navSidebar.querySelector("li.sidebar-item a.active");
+      if (activeItem) {
+        // Wait for the scroll height and height to resolve by observing size changes on the
+        // nav element that is scrollable
+        const resizeObserver = new ResizeObserver((_entries) => {
+          // The bottom of the element
+          const elBottom = activeItem.offsetTop;
+          const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight;
+
+          // The element height and scroll height are the same, then we are still loading
+          if (viewBottom !== navSidebar.scrollHeight) {
+            // Determine if the item isn't visible and scroll to it
+            if (elBottom >= viewBottom) {
+              navSidebar.scrollTop = elBottom;
+            }
+
+            // stop observing now since we've completed the scroll
+            resizeObserver.unobserve(navSidebar);
+          }
+        });
+        resizeObserver.observe(navSidebar);
+      }
+    }
+  }
+});
diff --git a/docs/site_libs/quarto-search/autocomplete.umd.js b/docs/site_libs/quarto-search/autocomplete.umd.js
new file mode 100644
index 00000000..619c57cc
--- /dev/null
+++ b/docs/site_libs/quarto-search/autocomplete.umd.js
@@ -0,0 +1,3 @@
+/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof globalThis?globalThis:e||self)["@algolia/autocomplete-js"]={})}(this,(function(e){"use strict";function t(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function n(e){for(var n=1;n<arguments.length;n++){var r=null!=arguments[n]?arguments[n]:{};n%2?t(Object(r),!0).forEach((function(t){o(e,t,r[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(r)):t(Object(r)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(r,t))}))}return e}function r(e){return r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof 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n=Object.prototype.toString.call(e).slice(8,-1);return"Object"===n&&e.constructor&&(n=e.constructor.name),"Map"===n||"Set"===n?Array.from(e):"Arguments"===n||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(n)?s(e,t):void 0}}function s(e,t){(null==t||t>e.length)&&(t=e.length);for(var n=0,r=new Array(t);n<t;n++)r[n]=e[n];return r}function p(e){return{current:e}}function f(e,t){var n=void 0;return function(){for(var r=arguments.length,o=new Array(r),i=0;i<r;i++)o[i]=arguments[i];n&&clearTimeout(n),n=setTimeout((function(){return e.apply(void 0,o)}),t)}}function d(e){return e.reduce((function(e,t){return e.concat(t)}),[])}var m=0;function v(){return"autocomplete-".concat(m++)}function h(e,t){return t.reduce((function(e,t){return e&&e[t]}),e)}function g(e){return 0===e.collections.length?0:e.collections.reduce((function(e,t){return e+t.items.length}),0)}var y=function(){},b="1.7.3",O=[{segment:"autocomplete-core",version:b}];function _(e,t){var n=t;return{then:function(t,r){return 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+
diff --git a/docs/site_libs/quarto-search/fuse.min.js b/docs/site_libs/quarto-search/fuse.min.js
new file mode 100644
index 00000000..adc28356
--- /dev/null
+++ b/docs/site_libs/quarto-search/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.6.2 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2022 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;n<arguments.length;n++){var r=null!=arguments[n]?arguments[n]:{};n%2?e(Object(r),!0).forEach((function(e){c(t,e,r[e])})):Object.getOwnPropertyDescriptors?Object.defineProperties(t,Object.getOwnPropertyDescriptors(r)):e(Object(r)).forEach((function(e){Object.defineProperty(t,e,Object.getOwnPropertyDescriptor(r,e))}))}return t}function n(e){return n="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},n(e)}function r(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function i(e,t){for(var n=0;n<t.length;n++){var 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c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u<h;u+=1){var l=e(n.children[u],r,i);if(l.length)s.push.apply(s,f(l));else if(n.operator===ie)return[]}return s},i=this._myIndex.records,o={},c=[];return i.forEach((function(e){var t=e.$,i=e.i;if(k(t)){var a=r(n,t,i);a.length&&(o[i]||(o[i]={idx:i,item:t,matches:[]},c.push(o[i])),a.forEach((function(e){var t,n=e.matches;(t=o[i].matches).push.apply(t,f(n))})))}})),c}},{key:"_searchObjectList",value:function(e){var t=this,n=re(e,this.options),r=this._myIndex,i=r.keys,o=r.records,c=[];return o.forEach((function(e){var r=e.$,o=e.i;if(k(r)){var a=[];i.forEach((function(e,i){a.push.apply(a,f(t._findMatches({key:e,value:r[i],searcher:n})))})),a.length&&c.push({idx:o,item:r,matches:a})}})),c}},{key:"_findMatches",value:function(e){var t=e.key,n=e.value,r=e.searcher;if(!k(n))return[];var i=[];if(v(n))n.forEach((function(e){var n=e.v,o=e.i,c=e.n;if(k(n)){var a=r.searchIn(n),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:n,idx:o,norm:c,indices:h})}}));else{var o=n.v,c=n.n,a=r.searchIn(o),s=a.isMatch,u=a.score,h=a.indices;s&&i.push({score:u,key:t,value:o,norm:c,indices:h})}return i}}]),e}();return ye.version="6.6.2",ye.createIndex=F,ye.parseIndex=function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t();
\ No newline at end of file
diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js
new file mode 100644
index 00000000..f5d852d1
--- /dev/null
+++ b/docs/site_libs/quarto-search/quarto-search.js
@@ -0,0 +1,1140 @@
+const kQueryArg = "q";
+const kResultsArg = "show-results";
+
+// If items don't provide a URL, then both the navigator and the onSelect
+// function aren't called (and therefore, the default implementation is used)
+//
+// We're using this sentinel URL to signal to those handlers that this
+// item is a more item (along with the type) and can be handled appropriately
+const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05";
+
+window.document.addEventListener("DOMContentLoaded", function (_event) {
+  // Ensure that search is available on this page. If it isn't,
+  // should return early and not do anything
+  var searchEl = window.document.getElementById("quarto-search");
+  if (!searchEl) return;
+
+  const { autocomplete } = window["@algolia/autocomplete-js"];
+
+  let quartoSearchOptions = {};
+  let language = {};
+  const searchOptionEl = window.document.getElementById(
+    "quarto-search-options"
+  );
+  if (searchOptionEl) {
+    const jsonStr = searchOptionEl.textContent;
+    quartoSearchOptions = JSON.parse(jsonStr);
+    language = quartoSearchOptions.language;
+  }
+
+  // note the search mode
+  if (quartoSearchOptions.type === "overlay") {
+    searchEl.classList.add("type-overlay");
+  } else {
+    searchEl.classList.add("type-textbox");
+  }
+
+  // Used to determine highlighting behavior for this page
+  // A `q` query param is expected when the user follows a search
+  // to this page
+  const currentUrl = new URL(window.location);
+  const query = currentUrl.searchParams.get(kQueryArg);
+  const showSearchResults = currentUrl.searchParams.get(kResultsArg);
+  const mainEl = window.document.querySelector("main");
+
+  // highlight matches on the page
+  if (query !== null && mainEl) {
+    // perform any highlighting
+    highlight(escapeRegExp(query), mainEl);
+
+    // fix up the URL to remove the q query param
+    const replacementUrl = new URL(window.location);
+    replacementUrl.searchParams.delete(kQueryArg);
+    window.history.replaceState({}, "", replacementUrl);
+  }
+
+  // function to clear highlighting on the page when the search query changes
+  // (e.g. if the user edits the query or clears it)
+  let highlighting = true;
+  const resetHighlighting = (searchTerm) => {
+    if (mainEl && highlighting && query !== null && searchTerm !== query) {
+      clearHighlight(query, mainEl);
+      highlighting = false;
+    }
+  };
+
+  // Clear search highlighting when the user scrolls sufficiently
+  const resetFn = () => {
+    resetHighlighting("");
+    window.removeEventListener("quarto-hrChanged", resetFn);
+    window.removeEventListener("quarto-sectionChanged", resetFn);
+  };
+
+  // Register this event after the initial scrolling and settling of events
+  // on the page
+  window.addEventListener("quarto-hrChanged", resetFn);
+  window.addEventListener("quarto-sectionChanged", resetFn);
+
+  // Responsively switch to overlay mode if the search is present on the navbar
+  // Note that switching the sidebar to overlay mode requires more coordinate (not just
+  // the media query since we generate different HTML for sidebar overlays than we do
+  // for sidebar input UI)
+  const detachedMediaQuery =
+    quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)";
+
+  // If configured, include the analytics client to send insights
+  const plugins = configurePlugins(quartoSearchOptions);
+
+  let lastState = null;
+  const { setIsOpen, setQuery, setCollections } = autocomplete({
+    container: searchEl,
+    detachedMediaQuery: detachedMediaQuery,
+    defaultActiveItemId: 0,
+    panelContainer: "#quarto-search-results",
+    panelPlacement: quartoSearchOptions["panel-placement"],
+    debug: false,
+    openOnFocus: true,
+    plugins,
+    classNames: {
+      form: "d-flex",
+    },
+    translations: {
+      clearButtonTitle: language["search-clear-button-title"],
+      detachedCancelButtonText: language["search-detached-cancel-button-title"],
+      submitButtonTitle: language["search-submit-button-title"],
+    },
+    initialState: {
+      query,
+    },
+    getItemUrl({ item }) {
+      return item.href;
+    },
+    onStateChange({ state }) {
+      // Perhaps reset highlighting
+      resetHighlighting(state.query);
+
+      // If the panel just opened, ensure the panel is positioned properly
+      if (state.isOpen) {
+        if (lastState && !lastState.isOpen) {
+          setTimeout(() => {
+            positionPanel(quartoSearchOptions["panel-placement"]);
+          }, 150);
+        }
+      }
+
+      // Perhaps show the copy link
+      showCopyLink(state.query, quartoSearchOptions);
+
+      lastState = state;
+    },
+    reshape({ sources, state }) {
+      return sources.map((source) => {
+        try {
+          const items = source.getItems();
+
+          // Validate the items
+          validateItems(items);
+
+          // group the items by document
+          const groupedItems = new Map();
+          items.forEach((item) => {
+            const hrefParts = item.href.split("#");
+            const baseHref = hrefParts[0];
+            const isDocumentItem = hrefParts.length === 1;
+
+            const items = groupedItems.get(baseHref);
+            if (!items) {
+              groupedItems.set(baseHref, [item]);
+            } else {
+              // If the href for this item matches the document
+              // exactly, place this item first as it is the item that represents
+              // the document itself
+              if (isDocumentItem) {
+                items.unshift(item);
+              } else {
+                items.push(item);
+              }
+              groupedItems.set(baseHref, items);
+            }
+          });
+
+          const reshapedItems = [];
+          let count = 1;
+          for (const [_key, value] of groupedItems) {
+            const firstItem = value[0];
+            reshapedItems.push({
+              ...firstItem,
+              type: kItemTypeDoc,
+            });
+
+            const collapseMatches = quartoSearchOptions["collapse-after"];
+            const collapseCount =
+              typeof collapseMatches === "number" ? collapseMatches : 1;
+
+            if (value.length > 1) {
+              const target = `search-more-${count}`;
+              const isExpanded =
+                state.context.expanded &&
+                state.context.expanded.includes(target);
+
+              const remainingCount = value.length - collapseCount;
+
+              for (let i = 1; i < value.length; i++) {
+                if (collapseMatches && i === collapseCount) {
+                  reshapedItems.push({
+                    target,
+                    title: isExpanded
+                      ? language["search-hide-matches-text"]
+                      : remainingCount === 1
+                      ? `${remainingCount} ${language["search-more-match-text"]}`
+                      : `${remainingCount} ${language["search-more-matches-text"]}`,
+                    type: kItemTypeMore,
+                    href: kItemTypeMoreHref,
+                  });
+                }
+
+                if (isExpanded || !collapseMatches || i < collapseCount) {
+                  reshapedItems.push({
+                    ...value[i],
+                    type: kItemTypeItem,
+                    target,
+                  });
+                }
+              }
+            }
+            count += 1;
+          }
+
+          return {
+            ...source,
+            getItems() {
+              return reshapedItems;
+            },
+          };
+        } catch (error) {
+          // Some form of error occurred
+          return {
+            ...source,
+            getItems() {
+              return [
+                {
+                  title: error.name || "An Error Occurred While Searching",
+                  text:
+                    error.message ||
+                    "An unknown error occurred while attempting to perform the requested search.",
+                  type: kItemTypeError,
+                },
+              ];
+            },
+          };
+        }
+      });
+    },
+    navigator: {
+      navigate({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.location.assign(itemUrl);
+        }
+      },
+      navigateNewTab({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          const windowReference = window.open(itemUrl, "_blank", "noopener");
+          if (windowReference) {
+            windowReference.focus();
+          }
+        }
+      },
+      navigateNewWindow({ itemUrl }) {
+        if (itemUrl !== offsetURL(kItemTypeMoreHref)) {
+          window.open(itemUrl, "_blank", "noopener");
+        }
+      },
+    },
+    getSources({ state, setContext, setActiveItemId, refresh }) {
+      return [
+        {
+          sourceId: "documents",
+          getItemUrl({ item }) {
+            if (item.href) {
+              return offsetURL(item.href);
+            } else {
+              return undefined;
+            }
+          },
+          onSelect({
+            item,
+            state,
+            setContext,
+            setIsOpen,
+            setActiveItemId,
+            refresh,
+          }) {
+            if (item.type === kItemTypeMore) {
+              toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+
+              // Toggle more
+              setIsOpen(true);
+            }
+          },
+          getItems({ query }) {
+            if (query === null || query === "") {
+              return [];
+            }
+
+            const limit = quartoSearchOptions.limit;
+            if (quartoSearchOptions.algolia) {
+              return algoliaSearch(query, limit, quartoSearchOptions.algolia);
+            } else {
+              // Fuse search options
+              const fuseSearchOptions = {
+                isCaseSensitive: false,
+                shouldSort: true,
+                minMatchCharLength: 2,
+                limit: limit,
+              };
+
+              return readSearchData().then(function (fuse) {
+                return fuseSearch(query, fuse, fuseSearchOptions);
+              });
+            }
+          },
+          templates: {
+            noResults({ createElement }) {
+              const hasQuery = lastState.query;
+
+              return createElement(
+                "div",
+                {
+                  class: `quarto-search-no-results${
+                    hasQuery ? "" : " no-query"
+                  }`,
+                },
+                language["search-no-results-text"]
+              );
+            },
+            header({ items, createElement }) {
+              // count the documents
+              const count = items.filter((item) => {
+                return item.type === kItemTypeDoc;
+              }).length;
+
+              if (count > 0) {
+                return createElement(
+                  "div",
+                  { class: "search-result-header" },
+                  `${count} ${language["search-matching-documents-text"]}`
+                );
+              } else {
+                return createElement(
+                  "div",
+                  { class: "search-result-header-no-results" },
+                  ``
+                );
+              }
+            },
+            footer({ _items, createElement }) {
+              if (
+                quartoSearchOptions.algolia &&
+                quartoSearchOptions.algolia["show-logo"]
+              ) {
+                const libDir = quartoSearchOptions.algolia["libDir"];
+                const logo = createElement("img", {
+                  src: offsetURL(
+                    `${libDir}/quarto-search/search-by-algolia.svg`
+                  ),
+                  class: "algolia-search-logo",
+                });
+                return createElement(
+                  "a",
+                  { href: "http://www.algolia.com/" },
+                  logo
+                );
+              }
+            },
+
+            item({ item, createElement }) {
+              return renderItem(
+                item,
+                createElement,
+                state,
+                setActiveItemId,
+                setContext,
+                refresh
+              );
+            },
+          },
+        },
+      ];
+    },
+  });
+
+  window.quartoOpenSearch = () => {
+    setIsOpen(false);
+    setIsOpen(true);
+    focusSearchInput();
+  };
+
+  // Remove the labeleledby attribute since it is pointing
+  // to a non-existent label
+  if (quartoSearchOptions.type === "overlay") {
+    const inputEl = window.document.querySelector(
+      "#quarto-search .aa-Autocomplete"
+    );
+    if (inputEl) {
+      inputEl.removeAttribute("aria-labelledby");
+    }
+  }
+
+  // If the main document scrolls dismiss the search results
+  // (otherwise, since they're floating in the document they can scroll with the document)
+  window.document.body.onscroll = () => {
+    setIsOpen(false);
+  };
+
+  if (showSearchResults) {
+    setIsOpen(true);
+    focusSearchInput();
+  }
+});
+
+function configurePlugins(quartoSearchOptions) {
+  const autocompletePlugins = [];
+  const algoliaOptions = quartoSearchOptions.algolia;
+  if (
+    algoliaOptions &&
+    algoliaOptions["analytics-events"] &&
+    algoliaOptions["search-only-api-key"] &&
+    algoliaOptions["application-id"]
+  ) {
+    const apiKey = algoliaOptions["search-only-api-key"];
+    const appId = algoliaOptions["application-id"];
+
+    // Aloglia insights may not be loaded because they require cookie consent
+    // Use deferred loading so events will start being recorded when/if consent
+    // is granted.
+    const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => {
+      if (
+        window.aa &&
+        window["@algolia/autocomplete-plugin-algolia-insights"]
+      ) {
+        window.aa("init", {
+          appId,
+          apiKey,
+          useCookie: true,
+        });
+
+        const { createAlgoliaInsightsPlugin } =
+          window["@algolia/autocomplete-plugin-algolia-insights"];
+        // Register the insights client
+        const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({
+          insightsClient: window.aa,
+          onItemsChange({ insights, insightsEvents }) {
+            const events = insightsEvents.map((event) => {
+              const maxEvents = event.objectIDs.slice(0, 20);
+              return {
+                ...event,
+                objectIDs: maxEvents,
+              };
+            });
+
+            insights.viewedObjectIDs(...events);
+          },
+        });
+        return algoliaInsightsPlugin;
+      }
+    });
+
+    // Add the plugin
+    autocompletePlugins.push(algoliaInsightsDeferredPlugin);
+    return autocompletePlugins;
+  }
+}
+
+// For plugins that may not load immediately, create a wrapper
+// plugin and forward events and plugin data once the plugin
+// is initialized. This is useful for cases like cookie consent
+// which may prevent the analytics insights event plugin from initializing
+// immediately.
+function deferredLoadPlugin(createPlugin) {
+  let plugin = undefined;
+  let subscribeObj = undefined;
+  const wrappedPlugin = () => {
+    if (!plugin && subscribeObj) {
+      plugin = createPlugin();
+      if (plugin && plugin.subscribe) {
+        plugin.subscribe(subscribeObj);
+      }
+    }
+    return plugin;
+  };
+
+  return {
+    subscribe: (obj) => {
+      subscribeObj = obj;
+    },
+    onStateChange: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onStateChange) {
+        plugin.onStateChange(obj);
+      }
+    },
+    onSubmit: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onSubmit) {
+        plugin.onSubmit(obj);
+      }
+    },
+    onReset: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.onReset) {
+        plugin.onReset(obj);
+      }
+    },
+    getSources: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.getSources) {
+        return plugin.getSources(obj);
+      } else {
+        return Promise.resolve([]);
+      }
+    },
+    data: (obj) => {
+      const plugin = wrappedPlugin();
+      if (plugin && plugin.data) {
+        plugin.data(obj);
+      }
+    },
+  };
+}
+
+function validateItems(items) {
+  // Validate the first item
+  if (items.length > 0) {
+    const item = items[0];
+    const missingFields = [];
+    if (item.href == undefined) {
+      missingFields.push("href");
+    }
+    if (!item.title == undefined) {
+      missingFields.push("title");
+    }
+    if (!item.text == undefined) {
+      missingFields.push("text");
+    }
+
+    if (missingFields.length === 1) {
+      throw {
+        name: `Error: Search index is missing the <code>${missingFields[0]}</code> field.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the <code>${missingFields[0]}</code> field or use <code>index-fields</code> in your <code>_quarto.yml</code> file to specify the field names.`,
+      };
+    } else if (missingFields.length > 1) {
+      const missingFieldList = missingFields
+        .map((field) => {
+          return `<code>${field}</code>`;
+        })
+        .join(", ");
+
+      throw {
+        name: `Error: Search index is missing the following fields: ${missingFieldList}.`,
+        message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use <code>index-fields</code> in your <code>_quarto.yml</code> file to specify the field names.`,
+      };
+    }
+  }
+}
+
+let lastQuery = null;
+function showCopyLink(query, options) {
+  const language = options.language;
+  lastQuery = query;
+  // Insert share icon
+  const inputSuffixEl = window.document.body.querySelector(
+    ".aa-Form .aa-InputWrapperSuffix"
+  );
+
+  if (inputSuffixEl) {
+    let copyButtonEl = window.document.body.querySelector(
+      ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton"
+    );
+
+    if (copyButtonEl === null) {
+      copyButtonEl = window.document.createElement("button");
+      copyButtonEl.setAttribute("class", "aa-CopyButton");
+      copyButtonEl.setAttribute("type", "button");
+      copyButtonEl.setAttribute("title", language["search-copy-link-title"]);
+      copyButtonEl.onmousedown = (e) => {
+        e.preventDefault();
+        e.stopPropagation();
+      };
+
+      const linkIcon = "bi-clipboard";
+      const checkIcon = "bi-check2";
+
+      const shareIconEl = window.document.createElement("i");
+      shareIconEl.setAttribute("class", `bi ${linkIcon}`);
+      copyButtonEl.appendChild(shareIconEl);
+      inputSuffixEl.prepend(copyButtonEl);
+
+      const clipboard = new window.ClipboardJS(".aa-CopyButton", {
+        text: function (_trigger) {
+          const copyUrl = new URL(window.location);
+          copyUrl.searchParams.set(kQueryArg, lastQuery);
+          copyUrl.searchParams.set(kResultsArg, "1");
+          return copyUrl.toString();
+        },
+      });
+      clipboard.on("success", function (e) {
+        // Focus the input
+
+        // button target
+        const button = e.trigger;
+        const icon = button.querySelector("i.bi");
+
+        // flash "checked"
+        icon.classList.add(checkIcon);
+        icon.classList.remove(linkIcon);
+        setTimeout(function () {
+          icon.classList.remove(checkIcon);
+          icon.classList.add(linkIcon);
+        }, 1000);
+      });
+    }
+
+    // If there is a query, show the link icon
+    if (copyButtonEl) {
+      if (lastQuery && options["copy-button"]) {
+        copyButtonEl.style.display = "flex";
+      } else {
+        copyButtonEl.style.display = "none";
+      }
+    }
+  }
+}
+
+/* Search Index Handling */
+// create the index
+var fuseIndex = undefined;
+async function readSearchData() {
+  // Initialize the search index on demand
+  if (fuseIndex === undefined) {
+    // create fuse index
+    const options = {
+      keys: [
+        { name: "title", weight: 20 },
+        { name: "section", weight: 20 },
+        { name: "text", weight: 10 },
+      ],
+      ignoreLocation: true,
+      threshold: 0.1,
+    };
+    const fuse = new window.Fuse([], options);
+
+    // fetch the main search.json
+    const response = await fetch(offsetURL("search.json"));
+    if (response.status == 200) {
+      return response.json().then(function (searchDocs) {
+        searchDocs.forEach(function (searchDoc) {
+          fuse.add(searchDoc);
+        });
+        fuseIndex = fuse;
+        return fuseIndex;
+      });
+    } else {
+      return Promise.reject(
+        new Error(
+          "Unexpected status from search index request: " + response.status
+        )
+      );
+    }
+  }
+  return fuseIndex;
+}
+
+function inputElement() {
+  return window.document.body.querySelector(".aa-Form .aa-Input");
+}
+
+function focusSearchInput() {
+  setTimeout(() => {
+    const inputEl = inputElement();
+    if (inputEl) {
+      inputEl.focus();
+    }
+  }, 50);
+}
+
+/* Panels */
+const kItemTypeDoc = "document";
+const kItemTypeMore = "document-more";
+const kItemTypeItem = "document-item";
+const kItemTypeError = "error";
+
+function renderItem(
+  item,
+  createElement,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh
+) {
+  switch (item.type) {
+    case kItemTypeDoc:
+      return createDocumentCard(
+        createElement,
+        "file-richtext",
+        item.title,
+        item.section,
+        item.text,
+        item.href
+      );
+    case kItemTypeMore:
+      return createMoreCard(
+        createElement,
+        item,
+        state,
+        setActiveItemId,
+        setContext,
+        refresh
+      );
+    case kItemTypeItem:
+      return createSectionCard(
+        createElement,
+        item.section,
+        item.text,
+        item.href
+      );
+    case kItemTypeError:
+      return createErrorCard(createElement, item.title, item.text);
+    default:
+      return undefined;
+  }
+}
+
+function createDocumentCard(createElement, icon, title, section, text, href) {
+  const iconEl = createElement("i", {
+    class: `bi bi-${icon} search-result-icon`,
+  });
+  const titleEl = createElement("p", { class: "search-result-title" }, title);
+  const titleContainerEl = createElement(
+    "div",
+    { class: "search-result-title-container" },
+    [iconEl, titleEl]
+  );
+
+  const textEls = [];
+  if (section) {
+    const sectionEl = createElement(
+      "p",
+      { class: "search-result-section" },
+      section
+    );
+    textEls.push(sectionEl);
+  }
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+  textEls.push(descEl);
+
+  const textContainerEl = createElement(
+    "div",
+    { class: "search-result-text-container" },
+    textEls
+  );
+
+  const containerEl = createElement(
+    "div",
+    {
+      class: "search-result-container",
+    },
+    [titleContainerEl, textContainerEl]
+  );
+
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    containerEl
+  );
+
+  const classes = ["search-result-doc", "search-item"];
+  if (!section) {
+    classes.push("document-selectable");
+  }
+
+  return createElement(
+    "div",
+    {
+      class: classes.join(" "),
+    },
+    linkEl
+  );
+}
+
+function createMoreCard(
+  createElement,
+  item,
+  state,
+  setActiveItemId,
+  setContext,
+  refresh
+) {
+  const moreCardEl = createElement(
+    "div",
+    {
+      class: "search-result-more search-item",
+      onClick: (e) => {
+        // Handle expanding the sections by adding the expanded
+        // section to the list of expanded sections
+        toggleExpanded(item, state, setContext, setActiveItemId, refresh);
+        e.stopPropagation();
+      },
+    },
+    item.title
+  );
+
+  return moreCardEl;
+}
+
+function toggleExpanded(item, state, setContext, setActiveItemId, refresh) {
+  const expanded = state.context.expanded || [];
+  if (expanded.includes(item.target)) {
+    setContext({
+      expanded: expanded.filter((target) => target !== item.target),
+    });
+  } else {
+    setContext({ expanded: [...expanded, item.target] });
+  }
+
+  refresh();
+  setActiveItemId(item.__autocomplete_id);
+}
+
+function createSectionCard(createElement, section, text, href) {
+  const sectionEl = createSection(createElement, section, text, href);
+  return createElement(
+    "div",
+    {
+      class: "search-result-doc-section search-item",
+    },
+    sectionEl
+  );
+}
+
+function createSection(createElement, title, text, href) {
+  const descEl = createElement("p", {
+    class: "search-result-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", { class: "search-result-section" }, title);
+  const linkEl = createElement(
+    "a",
+    {
+      href: offsetURL(href),
+      class: "search-result-link",
+    },
+    [titleEl, descEl]
+  );
+  return linkEl;
+}
+
+function createErrorCard(createElement, title, text) {
+  const descEl = createElement("p", {
+    class: "search-error-text",
+    dangerouslySetInnerHTML: {
+      __html: text,
+    },
+  });
+
+  const titleEl = createElement("p", {
+    class: "search-error-title",
+    dangerouslySetInnerHTML: {
+      __html: `<i class="bi bi-exclamation-circle search-error-icon"></i> ${title}`,
+    },
+  });
+  const errorEl = createElement("div", { class: "search-error" }, [
+    titleEl,
+    descEl,
+  ]);
+  return errorEl;
+}
+
+function positionPanel(pos) {
+  const panelEl = window.document.querySelector(
+    "#quarto-search-results .aa-Panel"
+  );
+  const inputEl = window.document.querySelector(
+    "#quarto-search .aa-Autocomplete"
+  );
+
+  if (panelEl && inputEl) {
+    panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`;
+    if (pos === "start") {
+      panelEl.style.left = `${Math.round(inputEl.left)}px`;
+    } else {
+      panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`;
+    }
+  }
+}
+
+/* Highlighting */
+// highlighting functions
+function highlightMatch(query, text) {
+  if (text) {
+    const start = text.toLowerCase().indexOf(query.toLowerCase());
+    if (start !== -1) {
+      const startMark = "<mark class='search-match'>";
+      const endMark = "</mark>";
+
+      const end = start + query.length;
+      text =
+        text.slice(0, start) +
+        startMark +
+        text.slice(start, end) +
+        endMark +
+        text.slice(end);
+      const startInfo = clipStart(text, start);
+      const endInfo = clipEnd(
+        text,
+        startInfo.position + startMark.length + endMark.length
+      );
+      text =
+        startInfo.prefix +
+        text.slice(startInfo.position, endInfo.position) +
+        endInfo.suffix;
+
+      return text;
+    } else {
+      return text;
+    }
+  } else {
+    return text;
+  }
+}
+
+function clipStart(text, pos) {
+  const clipStart = pos - 50;
+  if (clipStart < 0) {
+    // This will just return the start of the string
+    return {
+      position: 0,
+      prefix: "",
+    };
+  } else {
+    // We're clipping before the start of the string, walk backwards to the first space.
+    const spacePos = findSpace(text, pos, -1);
+    return {
+      position: spacePos.position,
+      prefix: "",
+    };
+  }
+}
+
+function clipEnd(text, pos) {
+  const clipEnd = pos + 200;
+  if (clipEnd > text.length) {
+    return {
+      position: text.length,
+      suffix: "",
+    };
+  } else {
+    const spacePos = findSpace(text, clipEnd, 1);
+    return {
+      position: spacePos.position,
+      suffix: spacePos.clipped ? "…" : "",
+    };
+  }
+}
+
+function findSpace(text, start, step) {
+  let stepPos = start;
+  while (stepPos > -1 && stepPos < text.length) {
+    const char = text[stepPos];
+    if (char === " " || char === "," || char === ":") {
+      return {
+        position: step === 1 ? stepPos : stepPos - step,
+        clipped: stepPos > 1 && stepPos < text.length,
+      };
+    }
+    stepPos = stepPos + step;
+  }
+
+  return {
+    position: stepPos - step,
+    clipped: false,
+  };
+}
+
+// removes highlighting as implemented by the mark tag
+function clearHighlight(searchterm, el) {
+  const childNodes = el.childNodes;
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+    if (node.nodeType === Node.ELEMENT_NODE) {
+      if (
+        node.tagName === "MARK" &&
+        node.innerText.toLowerCase() === searchterm.toLowerCase()
+      ) {
+        el.replaceChild(document.createTextNode(node.innerText), node);
+      } else {
+        clearHighlight(searchterm, node);
+      }
+    }
+  }
+}
+
+function escapeRegExp(string) {
+  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+}
+
+// highlight matches
+function highlight(term, el) {
+  const termRegex = new RegExp(term, "ig");
+  const childNodes = el.childNodes;
+
+  // walk back to front avoid mutating elements in front of us
+  for (let i = childNodes.length - 1; i >= 0; i--) {
+    const node = childNodes[i];
+
+    if (node.nodeType === Node.TEXT_NODE) {
+      // Search text nodes for text to highlight
+      const text = node.nodeValue;
+
+      let startIndex = 0;
+      let matchIndex = text.search(termRegex);
+      if (matchIndex > -1) {
+        const markFragment = document.createDocumentFragment();
+        while (matchIndex > -1) {
+          const prefix = text.slice(startIndex, matchIndex);
+          markFragment.appendChild(document.createTextNode(prefix));
+
+          const mark = document.createElement("mark");
+          mark.appendChild(
+            document.createTextNode(
+              text.slice(matchIndex, matchIndex + term.length)
+            )
+          );
+          markFragment.appendChild(mark);
+
+          startIndex = matchIndex + term.length;
+          matchIndex = text.slice(startIndex).search(new RegExp(term, "ig"));
+          if (matchIndex > -1) {
+            matchIndex = startIndex + matchIndex;
+          }
+        }
+        if (startIndex < text.length) {
+          markFragment.appendChild(
+            document.createTextNode(text.slice(startIndex, text.length))
+          );
+        }
+
+        el.replaceChild(markFragment, node);
+      }
+    } else if (node.nodeType === Node.ELEMENT_NODE) {
+      // recurse through elements
+      highlight(term, node);
+    }
+  }
+}
+
+/* Link Handling */
+// get the offset from this page for a given site root relative url
+function offsetURL(url) {
+  var offset = getMeta("quarto:offset");
+  return offset ? offset + url : url;
+}
+
+// read a meta tag value
+function getMeta(metaName) {
+  var metas = window.document.getElementsByTagName("meta");
+  for (let i = 0; i < metas.length; i++) {
+    if (metas[i].getAttribute("name") === metaName) {
+      return metas[i].getAttribute("content");
+    }
+  }
+  return "";
+}
+
+function algoliaSearch(query, limit, algoliaOptions) {
+  const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"];
+
+  const applicationId = algoliaOptions["application-id"];
+  const searchOnlyApiKey = algoliaOptions["search-only-api-key"];
+  const indexName = algoliaOptions["index-name"];
+  const indexFields = algoliaOptions["index-fields"];
+  const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey);
+  const searchParams = algoliaOptions["params"];
+  const searchAnalytics = !!algoliaOptions["analytics-events"];
+
+  return getAlgoliaResults({
+    searchClient,
+    queries: [
+      {
+        indexName: indexName,
+        query,
+        params: {
+          hitsPerPage: limit,
+          clickAnalytics: searchAnalytics,
+          ...searchParams,
+        },
+      },
+    ],
+    transformResponse: (response) => {
+      if (!indexFields) {
+        return response.hits.map((hit) => {
+          return hit.map((item) => {
+            return {
+              ...item,
+              text: highlightMatch(query, item.text),
+            };
+          });
+        });
+      } else {
+        const remappedHits = response.hits.map((hit) => {
+          return hit.map((item) => {
+            const newItem = { ...item };
+            ["href", "section", "title", "text"].forEach((keyName) => {
+              const mappedName = indexFields[keyName];
+              if (
+                mappedName &&
+                item[mappedName] !== undefined &&
+                mappedName !== keyName
+              ) {
+                newItem[keyName] = item[mappedName];
+                delete newItem[mappedName];
+              }
+            });
+            newItem.text = highlightMatch(query, newItem.text);
+            return newItem;
+          });
+        });
+        return remappedHits;
+      }
+    },
+  });
+}
+
+function fuseSearch(query, fuse, fuseOptions) {
+  return fuse.search(query, fuseOptions).map((result) => {
+    const addParam = (url, name, value) => {
+      const anchorParts = url.split("#");
+      const baseUrl = anchorParts[0];
+      const sep = baseUrl.search("\\?") > 0 ? "&" : "?";
+      anchorParts[0] = baseUrl + sep + name + "=" + value;
+      return anchorParts.join("#");
+    };
+
+    return {
+      title: result.item.title,
+      section: result.item.section,
+      href: addParam(result.item.href, kQueryArg, query),
+      text: highlightMatch(query, result.item.text),
+    };
+  });
+}
diff --git a/notebooks/Manifest.toml b/notebooks/Manifest.toml
index 49e756b6..b9291b54 100644
--- a/notebooks/Manifest.toml
+++ b/notebooks/Manifest.toml
@@ -2,7 +2,7 @@
 
 julia_version = "1.8.5"
 manifest_format = "2.0"
-project_hash = "38a34f0cc2271777d881bdfe6cc3a2c09bd2663c"
+project_hash = "4fe18aac5a948287125469c8cb7809a9229a4ede"
 
 [[deps.AbstractFFTs]]
 deps = ["ChainRulesCore", "LinearAlgebra"]
@@ -123,7 +123,7 @@ uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
 version = "1.0.8+0"
 
 [[deps.CCE]]
-deps = ["CategoricalArrays", "ChainRules", "ConformalPrediction", "CounterfactualExplanations", "Distances", "Distributions", "Flux", "JointEnergyModels", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "MLUtils", "Parameters", "Plots", "Random", "SliceMap", "Statistics", "StatsBase", "StatsPlots", "Term"]
+deps = ["CategoricalArrays", "ChainRules", "ConformalPrediction", "CounterfactualExplanations", "Distances", "Distributions", "Flux", "JointEnergyModels", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "MLUtils", "Parameters", "PkgTemplates", "Plots", "Random", "SliceMap", "Statistics", "StatsBase", "StatsPlots", "Term"]
 path = ".."
 uuid = "0232c203-4013-4b0d-ad96-43e3e11ac3bf"
 version = "0.1.0"
@@ -304,7 +304,7 @@ uuid = "ed09eef8-17a6-5b46-8889-db040fac31e3"
 version = "0.3.2"
 
 [[deps.ConformalPrediction]]
-deps = ["CategoricalArrays", "ChainRules", "Flux", "LinearAlgebra", "MLJBase", "MLJModelInterface", "NaturalSort", "Plots", "StatsBase"]
+deps = ["CategoricalArrays", "ChainRules", "Flux", "LinearAlgebra", "MLJBase", "MLJFlux", "MLJModelInterface", "NaturalSort", "Plots", "StatsBase"]
 path = "../../ConformalPrediction.jl"
 uuid = "98bfc277-1877-43dc-819b-a3e38c30242f"
 version = "0.1.6"
@@ -1252,6 +1252,12 @@ version = "1.1.0"
 [[deps.Mmap]]
 uuid = "a63ad114-7e13-5084-954f-fe012c677804"
 
+[[deps.Mocking]]
+deps = ["Compat", "ExprTools"]
+git-tree-sha1 = "782e258e80d68a73d8c916e55f8ced1de00c2cea"
+uuid = "78c3b35d-d492-501b-9361-3d52fe80e533"
+version = "0.7.6"
+
 [[deps.MosaicViews]]
 deps = ["MappedArrays", "OffsetArrays", "PaddedViews", "StackViews"]
 git-tree-sha1 = "7b86a5d4d70a9f5cdf2dacb3cbe6d251d1a61dbe"
@@ -1268,6 +1274,12 @@ git-tree-sha1 = "91a48569383df24f0fd2baf789df2aade3d0ad80"
 uuid = "6f286f6a-111f-5878-ab1e-185364afe411"
 version = "0.10.1"
 
+[[deps.Mustache]]
+deps = ["Printf", "Tables"]
+git-tree-sha1 = "87c371d27dbf2449a5685652ab322be163269df0"
+uuid = "ffc61752-8dc7-55ee-8c37-f3e9cdd09e70"
+version = "1.0.15"
+
 [[deps.MyterialColors]]
 git-tree-sha1 = "01d8466fb449436348999d7c6ad740f8f853a579"
 uuid = "1c23619d-4212-4747-83aa-717207fae70f"
@@ -1485,6 +1497,12 @@ deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markd
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 version = "1.8.0"
 
+[[deps.PkgTemplates]]
+deps = ["Dates", "InteractiveUtils", "LibGit2", "Mocking", "Mustache", "Parameters", "Pkg", "REPL", "UUIDs"]
+git-tree-sha1 = "e93643cc634d7551f68b63739d205d22216cec7c"
+uuid = "14b8a8f1-9102-5b29-a752-f990bacb7fe1"
+version = "0.7.32"
+
 [[deps.PkgVersion]]
 deps = ["Pkg"]
 git-tree-sha1 = "f6cf8e7944e50901594838951729a1861e668cb8"
diff --git a/notebooks/Project.toml b/notebooks/Project.toml
index 77c87108..b312e766 100644
--- a/notebooks/Project.toml
+++ b/notebooks/Project.toml
@@ -1,5 +1,6 @@
 [deps]
 CCE = "0232c203-4013-4b0d-ad96-43e3e11ac3bf"
+CategoricalDistributions = "af321ab8-2d2e-40a6-b165-3d674595d28e"
 ConformalPrediction = "98bfc277-1877-43dc-819b-a3e38c30242f"
 CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -9,6 +10,7 @@ JointEnergyModels = "48c56d24-211d-4463-bbc0-7a701b291131"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
+MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
diff --git a/notebooks/intro.qmd b/notebooks/intro.qmd
index 866830b7..3359fcf3 100644
--- a/notebooks/intro.qmd
+++ b/notebooks/intro.qmd
@@ -58,7 +58,7 @@ Using the package, we can apply Split Conformal Prediction as follows:
 ```{julia}
 X = table(permutedims(counterfactual_data.X))
 y =  counterfactual_data.output_encoder.labels
-conf_model = conformal_model(clf; method=:simple_inductive)
+conf_model = conformal_model(clf; method=:trainable_simple_inductive)
 mach = machine(conf_model, X, y)
 fit!(mach)
 ```
@@ -95,7 +95,7 @@ The right panel of @fig-losses shows the configurable classification loss in the
 
 temp = 0.5
 p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=0, temp=temp)
-p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, target=1, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(2,2))
+p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, zoom=0, temp=temp, clim=nothing, loss_matrix=ones(2,2))
 plot(p1, p2, size=(800,320))
 ```
 
@@ -127,11 +127,11 @@ niter = 100
 nsamples = 100
 
 plts = []
-for target ∈ counterfactual_data.y_levels
+for (i,target) ∈ enumerate(counterfactual_data.y_levels)
     sampler = CCE.EnergySampler(M, counterfactual_data, target; niter=niter, nsamples=100)
     Xgen = rand(sampler, nsamples)
     plt = plot(M, counterfactual_data; target=target, zoom=-3,cbar=false)
-    scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=target,shape=:star,label="X|y=$target")
+    scatter!(Xgen[1,:],Xgen[2,:],alpha=0.5,color=i,shape=:star,label="X|y=$target")
     push!(plts, plt)
 end
 plot(plts..., layout=(1,length(plts)), size=(img_height*length(plts),img_height))
@@ -264,8 +264,8 @@ fit!(mach)
 #| label: fig-losses-multi
 #| fig-cap: "Illustration of the smooth size loss and the configurable classification loss."
 
-temp = 0.05
-p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=-1, temp=temp)
+temp = 0.5
+p1 = contourf(mach.model, mach.fitresult, X, y; plot_set_loss=true, zoom=-1, temp=temp, clim=nothing)
 p2 = contourf(mach.model, mach.fitresult, X, y; plot_classification_loss=true, zoom=-1, temp=temp, clim=nothing, loss_matrix=ones(4,4))
 plot(p1, p2, size=(800,320))
 ```
diff --git a/notebooks/proposal_files/execute-results/html.json b/notebooks/proposal_files/execute-results/html.json
index 9f577ad9..433db5eb 100644
--- a/notebooks/proposal_files/execute-results/html.json
+++ b/notebooks/proposal_files/execute-results/html.json
@@ -1,7 +1,7 @@
 {
-  "hash": "b359927c37941b81cb7bf792790a86f8",
+  "hash": "24ab407f04257b00a84f7dcaee456281",
   "result": {
-    "markdown": "---\ntitle: High-Fidelity Counterfactual Explanations through Conformal Prediction\nsubtitle: Research Proposal\nabstract: |\n    We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n---\n\n\n\n## Motivation\n\nCounterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n\n### Counterfactual Explanations or Adversarial Examples?\n\nMost state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n\n\n\n\n\n![You may not like it, but this is what stripped-down counterfactuals look like. Here we have used @wachter2017counterfactual to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).](www/you_may_not_like_it.png){#fig-adv}\n\nThe crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nIn the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n\n![Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).](www/mnist_9to4_latent.png){#fig-latent}\n\n> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n\n- Latent can be manipulated: \n    - train biased model\n    - train VAE with biased variable removed/attacked (use Boston housing dataset)\n    - hypothesis: will generate bias-free explanations\n\n### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n\nIn light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n\n::: {#prp-surrogate}\n\n## Avoid Surrogates\n\nSince we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n\n:::\n\nIn cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n\nIn the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n\nDoes fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n\n::: {#def-fidele}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nIn order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n\n1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n\nIf the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n\n> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n\n## Conformal Counterfactual Explanations\n\nIn @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n\n### Minimizing Predictive Uncertainty\n\n@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n\n- Problem: restricted to Bayesian models.\n- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n\n### Background on Conformal Prediction\n\n- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n- Conformal prediction is instance-based. So is CE. \n- Take any fitted model and turn it into a conformal model using calibration data.\n- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n\n> Does the coverage guarantee carry over to counterfactuals?\n\n### Generating Conformal Counterfactuals\n\nWhile Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n\n> Challenge: still need to implement these loss functions. \n\n## Experiments\n\n### Research Questions\n\n- Is CP alone enough to ensure realistic counterfactuals?\n- Do counterfactuals improve further as the models get better?\n- Do counterfactuals get more realistic as coverage\n- What happens as we vary coverage and setsize?\n- What happens as we improve the model robustness?\n- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n\n- Benchmarking:\n    - add PROBE [@pawelczyk2022probabilistically] into the mix.\n    - compare travel costs to domain shits.\n\n> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n\n## References\n\n",
+    "markdown": "---\ntitle: High-Fidelity Counterfactual Explanations through Conformal Prediction\nsubtitle: Research Proposal\nabstract: |\n    We propose Conformal Counterfactual Explanations: an effortless and rigorous way to produce realistic and faithful Counterfactual Explanations using Conformal Prediction. To address the need for realistic counterfactuals, existing work has primarily relied on separate generative models to learn the data-generating process. While this is an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces a significant engineering overhead but also reallocates the task of creating realistic model explanations from the model itself to the generative model. Recent work has shown that there is no need for any of this when working with probabilistic models that explicitly quantify their own uncertainty. Unfortunately, most models used in practice still do not fulfil that basic requirement, in which case we would like to have a way to quantify predictive uncertainty in a post-hoc fashion.\n---\n\n\n\n## Motivation\n\nCounterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models but also enable affected individuals to challenge them through the means of Algorithmic Recourse. \n\n### Counterfactual Explanations or Adversarial Examples?\n\nMost state-of-the-art approaches to generating Counterfactual Explanations (CE) rely on gradient descent in the feature space. The key idea is to perturb inputs $x\\in\\mathcal{X}$ into a black-box model $f: \\mathcal{X} \\mapsto \\mathcal{Y}$ in order to change the model output $f(x)$ to some pre-specified target value $t\\in\\mathcal{Y}$. Formally, this boils down to defining some loss function $\\ell(f(x),t)$ and taking gradient steps in the minimizing direction. The so-generated counterfactuals are considered valid as soon as the predicted label matches the target label. A stripped-down counterfactual explanation is therefore little different from an adversarial example. In @fig-adv, for example, generic counterfactual search as in @wachter2017counterfactual has been applied to MNIST data.\n\n\n\n\n\n\n\n![You may not like it, but this is what stripped-down counterfactuals look like. Here we have used @wachter2017counterfactual to generate multiple counterfactuals for turning an 8 (eight) into a 3 (three).](www/you_may_not_like_it.png){#fig-adv}\n\nThe crucial difference between adversarial examples and counterfactuals is one of intent. While adversarial examples are typically intended to go unnoticed, counterfactuals in the context of Explainable AI are generally sought to be \"plausible\", \"realistic\" or \"feasible\". To fulfil this latter goal, researchers have come up with a myriad of ways. @joshi2019realistic were among the first to suggest that instead of searching counterfactuals in the feature space, we can instead traverse a latent embedding learned by a surrogate generative model. Similarly, @poyiadzi2020face use density ... Finally, @karimi2021algorithmic argues that counterfactuals should comply with the causal model that generates them [CHECK IF WE CAN PHASE THIS LIKE THIS]. Other related approaches include ... All of these different approaches have a common goal: they aim to ensure that the generated counterfactuals comply with the (learned) data-generating process (DGB). \n\n::: {#def-plausible}\n\n## Plausible Counterfactuals\n\nFormally, if $x \\sim \\mathcal{X}$ and for the corresponding counterfactual we have $x^{\\prime}\\sim\\mathcal{X}^{\\prime}$, then for $x^{\\prime}$ to be considered a plausible counterfactual, we need: $\\mathcal{X} \\approxeq \\mathcal{X}^{\\prime}$.\n\n:::\n\nIn the context of Algorithmic Recourse, it makes sense to strive for plausible counterfactuals, since anything else would essentially require individuals to move to out-of-distribution states. But it is worth noting that our ambition to meet this goal, may have implications on our ability to faithfully explain the behaviour of the underlying black-box model (arguably our principal goal). By essentially decoupling the task of learning plausible representations of the data from the model itself, we open ourselves up to vulnerabilities. Using a separate generative model to learn $\\mathcal{X}$, for example, has very serious implications for the generated counterfactuals. @fig-latent compares the results of applying REVISE [@joshi2019realistic] to MNIST data using two different Variational Auto-Encoders: while the counterfactual generated using an expressive (strong) VAE is compelling, the result relying on a less expressive (weak) VAE is not even valid. In this latter case, the decoder step of the VAE fails to yield values in $\\mathcal{X}$ and hence the counterfactual search in the learned latent space is doomed. \n\n\n\n\n\n\n\n![Counterfactual explanations for MNIST using a Latent Space generator: turning a nine (9) into a four (4).](www/mnist_9to4_latent.png){#fig-latent}\n\n> Here it would be nice to have another example where we poison the data going into the generative model to hide biases present in the data (e.g. Boston housing).\n\n- Latent can be manipulated: \n    - train biased model\n    - train VAE with biased variable removed/attacked (use Boston housing dataset)\n    - hypothesis: will generate bias-free explanations\n\n### From Plausible to High-Fidelity Counterfactuals {#sec-fidelity}\n\nIn light of the findings, we propose to generally avoid using surrogate models to learn $\\mathcal{X}$ in the context of Counterfactual Explanations.\n\n::: {#prp-surrogate}\n\n## Avoid Surrogates\n\nSince we are in the business of explaining a black-box model, the task of learning realistic representations of the data should not be reallocated from the model itself to some surrogate model.\n\n:::\n\nIn cases where the use of surrogate models cannot be avoided, we propose to weigh the plausibility of counterfactuals against their fidelity to the black-box model. In the context of Explainable AI, fidelity is defined as describing how an explanation approximates the prediction of the black-box model [@molnar2020interpretable]. Fidelity has become the default metric for evaluating Local Model-Agnostic Models, since they often involve local surrogate models whose predictions need not always match those of the black-box model. \n\nIn the case of Counterfactual Explanations, the concept of fidelity has so far been ignored. This is not altogether surprising, since by construction and design, Counterfactual Explanations work with the predictions of the black-box model directly: as stated above, a counterfactual $x^{\\prime}$ is considered valid if and only if $f(x^{\\prime})=t$, where $t$ denote some target outcome. \n\nDoes fidelity even make sense in the context of CE, and if so, how can we define it? In light of the examples in the previous section, we think it is urgent to introduce a notion of fidelity in this context, that relates to the distributional properties of the generated counterfactuals. In particular, we propose that a high-fidelity counterfactual $x^{\\prime}$ complies with the class-conditional distribution $\\mathcal{X}_{\\theta} = p_{\\theta}(X|y)$ where $\\theta$ denote the black-box model parameters. \n\n::: {#def-fidele}\n\n## High-Fidelity Counterfactuals\n\nLet $\\mathcal{X}_{\\theta}|y = p_{\\theta}(X|y)$ denote the class-conditional distribution of $X$ defined by $\\theta$. Then for $x^{\\prime}$ to be considered a high-fidelity counterfactual, we need: $\\mathcal{X}_{\\theta}|t \\approxeq \\mathcal{X}^{\\prime}$ where $t$ denotes the target outcome.\n\n:::\n\nIn order to assess the fidelity of counterfactuals, we propose the following two-step procedure:\n\n1) Generate samples $X_{\\theta}|y$ and $X^{\\prime}$ from $\\mathcal{X}_{\\theta}|t$ and $\\mathcal{X}^{\\prime}$, respectively.\n2) Compute the Maximum Mean Discrepancy (MMD) between $X_{\\theta}|y$ and $X^{\\prime}$. \n\nIf the computed value is different from zero, we can reject the null-hypothesis of fidelity.\n\n> Two challenges here: 1) implementing the sampling procedure in @grathwohl2020your; 2) it is unclear if MMD is really the right way to measure this. \n\n## Conformal Counterfactual Explanations\n\nIn @sec-fidelity, we have advocated for avoiding surrogate models in the context of Counterfactual Explanations. In this section, we introduce an alternative way to generate high-fidelity Counterfactual Explanations. In particular, we propose Conformal Counterfactual Explanations (CCE), that is Counterfactual Explanations that minimize the predictive uncertainty of conformal models. \n\n### Minimizing Predictive Uncertainty\n\n@schut2021generating demonstrated that the goal of generating realistic (plausible) counterfactuals can also be achieved by seeking counterfactuals that minimize the predictive uncertainty of the underlying black-box model. Similarly, @antoran2020getting ...\n\n- Problem: restricted to Bayesian models.\n- Solution: post-hoc predictive uncertainty quantification. In particular, Conformal Prediction. \n\n### Background on Conformal Prediction\n\n- Distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification.\n- Conformal prediction is instance-based. So is CE. \n- Take any fitted model and turn it into a conformal model using calibration data.\n- Our approach, therefore, relaxes the restriction on the family of black-box models, at the cost of relying on a subset of the data. Arguably, data is often abundant and in most applications practitioners tend to hold out a test data set anyway. \n\n> Does the coverage guarantee carry over to counterfactuals?\n\n### Generating Conformal Counterfactuals\n\nWhile Conformal Prediction has recently grown in popularity, it does introduce a challenge in the context of classification: the predictions of Conformal Classifiers are set-valued and therefore difficult to work with, since they are, for example, non-differentiable. Fortunately, @stutz2022learning introduced carefully designed differentiable loss functions that make it possible to evaluate the performance of conformal predictions in training. We can leverage these recent advances in the context of gradient-based counterfactual search ...\n\n> Challenge: still need to implement these loss functions. \n\n## Experiments\n\n### Research Questions\n\n- Is CP alone enough to ensure realistic counterfactuals?\n- Do counterfactuals improve further as the models get better?\n- Do counterfactuals get more realistic as coverage\n- What happens as we vary coverage and setsize?\n- What happens as we improve the model robustness?\n- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?\n- What happens if we combine with DiCE, ClaPROAR, Gravitational?\n- What about CE robustness to endogenous shifts [@altmeyer2023endogenous]?\n\n- Benchmarking:\n    - add PROBE [@pawelczyk2022probabilistically] into the mix.\n    - compare travel costs to domain shits.\n\n> Nice to have: What about using Laplace Approximation, then Conformal Prediction? What about using Conformalised Laplace? \n\n## References\n\n",
     "supporting": [
       "proposal_files/figure-html"
     ],
diff --git a/paper/paper.pdf b/paper/paper.pdf
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diff --git a/src/CCE.jl b/src/CCE.jl
index c8729d9a..da9ffd9e 100644
--- a/src/CCE.jl
+++ b/src/CCE.jl
@@ -10,4 +10,7 @@ include("generator.jl")
 include("sampling.jl")
 # include("ConformalGenerator.jl")
 
+using MLJFlux
+MLJFlux.reformat(X, ::Type{<:AbstractMatrix}) = permutedims(X)
+
 end
diff --git a/src/model.jl b/src/model.jl
index 80fb20eb..bb23d804 100644
--- a/src/model.jl
+++ b/src/model.jl
@@ -56,15 +56,7 @@ In the binary case logits are fed through the sigmoid function instead of softma
 which follows from the derivation here: https://stats.stackexchange.com/questions/233658/softmax-vs-sigmoid-function-in-logistic-classifier
 """
 function Models.logits(M::ConformalModel, X::AbstractArray)
-    conf_model = M.model
     fitresult = M.fitresult
-    # x = MLJBase.table(permutedims(x))
-    # p̂ = MMI.predict(conf_model.model, fitresult, MMI.reformat(conf_model.model, x)...)
-    # p̂ = map(p̂) do pp
-    #     L = p̂.decoder.classes
-    #     probas = pdf.(pp, L)
-    #     return probas
-    # end
     function predict_logits(fitresult, x)
         p̂ = fitresult[1](x)
         if ndims(p̂) == 2
diff --git a/src/penalties.jl b/src/penalties.jl
index 3f5751cb..161e2ac2 100644
--- a/src/penalties.jl
+++ b/src/penalties.jl
@@ -19,7 +19,7 @@ function set_size_penalty(
         x = Matrix(x)
         if target_probs(counterfactual_explanation, x)[1] >= 0.5
             l = ConformalPrediction.smooth_size_loss(
-                conf_model, fitresult, x;
+                conf_model, fitresult, x';
                 κ=κ,
                 temp=temp
             )[1]
-- 
GitLab