From 93228304412e9cb368a8de673dedfb1629d5c9bd Mon Sep 17 00:00:00 2001
From: pat-alt <altmeyerpat@gmail.com>
Date: Sat, 11 Feb 2023 16:44:30 -0500
Subject: [PATCH] proposal
---
.gitignore | 1 +
Manifest.toml | 1883 +++++++++++++
Project.toml | 4 +
.../pat-alt/documenterjl/_extension.yml | 10 +
_extensions/pat-alt/julia/_extension.yml | 13 +
_extensions/pat-alt/julia/custom.scss | 54 +
_extensions/pat-alt/julia/revealjs.scss | 15 +
_quarto.yml | 10 +
bib.bib | 2406 +++++++++++++++++
build/dev/proposal.html | 398 +++
.../libs/bootstrap/bootstrap-icons.css | 2018 ++++++++++++++
.../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 164168 bytes
.../libs/bootstrap/bootstrap.min.css | 10 +
.../libs/bootstrap/bootstrap.min.js | 7 +
.../libs/clipboard/clipboard.min.js | 7 +
.../libs/quarto-html/anchor.min.js | 9 +
.../libs/quarto-html/popper.min.js | 6 +
.../quarto-syntax-highlighting.css | 171 ++
.../proposal_files/libs/quarto-html/quarto.js | 840 ++++++
.../proposal_files/libs/quarto-html/tippy.css | 1 +
.../libs/quarto-html/tippy.umd.min.js | 2 +
dev/proposal.qmd | 63 +
22 files changed, 7928 insertions(+)
create mode 100644 .gitignore
create mode 100644 Manifest.toml
create mode 100644 Project.toml
create mode 100644 _extensions/pat-alt/documenterjl/_extension.yml
create mode 100644 _extensions/pat-alt/julia/_extension.yml
create mode 100644 _extensions/pat-alt/julia/custom.scss
create mode 100644 _extensions/pat-alt/julia/revealjs.scss
create mode 100644 _quarto.yml
create mode 100644 bib.bib
create mode 100644 build/dev/proposal.html
create mode 100644 build/dev/proposal_files/libs/bootstrap/bootstrap-icons.css
create mode 100644 build/dev/proposal_files/libs/bootstrap/bootstrap-icons.woff
create mode 100644 build/dev/proposal_files/libs/bootstrap/bootstrap.min.css
create mode 100644 build/dev/proposal_files/libs/bootstrap/bootstrap.min.js
create mode 100644 build/dev/proposal_files/libs/clipboard/clipboard.min.js
create mode 100644 build/dev/proposal_files/libs/quarto-html/anchor.min.js
create mode 100644 build/dev/proposal_files/libs/quarto-html/popper.min.js
create mode 100644 build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css
create mode 100644 build/dev/proposal_files/libs/quarto-html/quarto.js
create mode 100644 build/dev/proposal_files/libs/quarto-html/tippy.css
create mode 100644 build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js
create mode 100644 dev/proposal.qmd
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..075b2542
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
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diff --git a/Manifest.toml b/Manifest.toml
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index 00000000..474ab778
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+++ b/Manifest.toml
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+
+[[deps.xkbcommon_jll]]
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+git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a"
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diff --git a/Project.toml b/Project.toml
new file mode 100644
index 00000000..620a1b28
--- /dev/null
+++ b/Project.toml
@@ -0,0 +1,4 @@
+[deps]
+ConformalPrediction = "98bfc277-1877-43dc-819b-a3e38c30242f"
+CounterfactualExplanations = "2f13d31b-18db-44c1-bc43-ebaf2cff0be0"
+MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
diff --git a/_extensions/pat-alt/documenterjl/_extension.yml b/_extensions/pat-alt/documenterjl/_extension.yml
new file mode 100644
index 00000000..542821d5
--- /dev/null
+++ b/_extensions/pat-alt/documenterjl/_extension.yml
@@ -0,0 +1,10 @@
+title: |
+ `Documenter.jl`
+author: pat-alt
+version: 1.0.0
+quarto-required: ">=99.9.0"
+contributes:
+ formats:
+ commonmark:
+ variant: -raw_html+tex_math_dollars
+ wrap: preserve
\ No newline at end of file
diff --git a/_extensions/pat-alt/julia/_extension.yml b/_extensions/pat-alt/julia/_extension.yml
new file mode 100644
index 00000000..07935cbf
--- /dev/null
+++ b/_extensions/pat-alt/julia/_extension.yml
@@ -0,0 +1,13 @@
+title: Julia
+author: pat-alt
+version: 1.0.0
+quarto-required: ">=99.9.0"
+contributes:
+ formats:
+ html:
+ toc: true
+ theme: [default, custom.scss]
+ revealjs:
+ theme: [default, custom.scss, revealjs.scss]
+
+
diff --git a/_extensions/pat-alt/julia/custom.scss b/_extensions/pat-alt/julia/custom.scss
new file mode 100644
index 00000000..0e651681
--- /dev/null
+++ b/_extensions/pat-alt/julia/custom.scss
@@ -0,0 +1,54 @@
+/*-- scss:defaults --*/
+@font-face {
+ font-family: JuliaMono-Light;
+ src: url("https://cdn.jsdelivr.net/gh/cormullion/juliamono/webfonts/JuliaMono-Light.woff2");
+}
+
+@font-face {
+ font-family: "Bangla MN";
+ src: url("https://db.onlinewebfonts.com/c/3d37467c4a9c7c1188bc3e0b7aadfa0d?family=Bangla+MN");
+}
+
+@import url("https://fonts.googleapis.com/css?family=Roboto");
+
+$font-family-monospace: "JuliaMono-Light" !default;
+$font-family-sans-serif: "Roboto" !default;
+
+// Colors
+// Define the Julia colours as per https://github.com/JuliaLang/julia-logo-graphics#color-definitions
+$julia-blue: #4063D8;
+$julia-green: #389836;
+$julia-purple: #9558B2;
+$julia-red: #CB3C33;
+// Assign:
+$primary: $julia-blue !default;
+$body-bg: #ffffff !default;
+$body-color: #000000 !default;
+$presentation-heading-color: $julia-blue !default;
+$link-color: $julia-blue !default;
+// Code:
+$code-color: $julia-green !default;
+$dark-bg-code-color: lighten($code-color, 15%) !default;
+$light-bg-code-color: darken($code-color, 15%) !default;
+$code-block-border-left: lighten($code-color, 15%) !default;
+$btn-code-copy-color: $julia-purple !default;
+$btn-code-copy-color-active: lighten($btn-code-copy-color, 15%) !default;
+// Toc:
+$toc-color: $julia-purple !default;
+$toc-active-border: $julia-purple !default;
+$toc-inactive-border: lighten($toc-active-border, 15%) !default;
+// Callouts:
+$callout-color-note: $julia-blue !default;
+$callout-color-tip: $julia-green !default;
+$callout-color-warning: $julia-purple !default;
+$callout-color-caution: lighten($callout-color-warning, 30%) !default;
+$callout-color-important: $julia-red !default;
+
+/*-- scss:rules --*/
+h1, h2, h3, h4, h5, h6 {
+ font-family: "JuliaMono-Light";
+}
+
+hr {
+ color: $julia-blue;
+}
\ No newline at end of file
diff --git a/_extensions/pat-alt/julia/revealjs.scss b/_extensions/pat-alt/julia/revealjs.scss
new file mode 100644
index 00000000..32aaea81
--- /dev/null
+++ b/_extensions/pat-alt/julia/revealjs.scss
@@ -0,0 +1,15 @@
+/*-- scss:rules --*/
+.reveal {
+
+ h1, h2, h3, h4, h5, h6 {
+ font-family: "JuliaMono-Light";
+ }
+
+ hr {
+ color: $julia-blue;
+ }
+
+ .progress {
+ color: $code-color;
+ }
+}
diff --git a/_quarto.yml b/_quarto.yml
new file mode 100644
index 00000000..4fbdb5b4
--- /dev/null
+++ b/_quarto.yml
@@ -0,0 +1,10 @@
+project:
+ output-dir: build
+
+bibliography: bib.bib
+
+format:
+ julia-html: default
+
+
+
diff --git a/bib.bib b/bib.bib
new file mode 100644
index 00000000..1b1dca6b
--- /dev/null
+++ b/bib.bib
@@ -0,0 +1,2406 @@
+@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}},
+ date = {2023},
+ title = {Endogenous {Macrodynamics} in {Algorithmic} {Recourse}},
+ file = {:altmeyerendogenous - Endogenous Macrodynamics in Algorithmic Recourse.pdf:PDF},
+}
+
+%% 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},
+ date = {2002},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Econometrica : journal of the Econometric Society},
+ number = {1},
+ pages = {91--117},
+ shortjournal = {Econometrica},
+ title = {Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings},
+ volume = {70}}
+
+@article{abadie2003economic,
+ author = {Abadie, Alberto and Gardeazabal, Javier},
+ date = {2003},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {American economic review},
+ number = {1},
+ pages = {113--132},
+ title = {The Economic Costs of Conflict: {{A}} Case Study of the {{Basque Country}}},
+ volume = {93}}
+
+@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}})},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {1--8},
+ publisher = {{IEEE}},
+ title = {Machine {{Learning Model Drift Detection Via Weak Data Slices}}}}
+
+@article{allen2017referencedependent,
+ author = {Allen, Eric J and Dechow, Patricia M and Pope, Devin G and Wu, George},
+ date = {2017},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Management Science},
+ number = {6},
+ pages = {1657--1672},
+ title = {Reference-Dependent Preferences: {{Evidence}} from Marathon Runners},
+ volume = {63}}
+
+@article{altmeyer2018option,
+ author = {Altmeyer, Patrick and Grapendal, Jacob Daniel and Pravosud, Makar and Quintana, Gand Derry},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ title = {Option Pricing in the {{Heston}} Stochastic Volatility Model: An Empirical Evaluation}}
+
+@article{altmeyer2021deep,
+ author = {Altmeyer, Patrick and Agusti, Marc and Vidal-Quadras Costa, Ignacio},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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}}
+
+@book{altmeyer2021deepvars,
+ author = {Altmeyer, Patrick},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ title = {Deepvars: {{Deep Vector Autoregession}}}}
+
+@misc{altmeyer2022counterfactualexplanations,
+ author = {Altmeyer, Patrick},
+ date = {2022},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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}}
+
+@software{altmeyerCounterfactualExplanationsJlJulia2022,
+ author = {Altmeyer, Patrick},
+ date = {2022},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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}}
+
+@unpublished{angelopoulos2021gentle,
+ archiveprefix = {arXiv},
+ author = {Angelopoulos, Anastasios N. and Bates, Stephen},
+ date = {2021},
+ 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},
+ title = {A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification}}
+
+@Misc{angelopoulos2022uncertainty,
+ author = {Angelopoulos, Anastasios and Bates, Stephen and Malik, Jitendra and Jordan, Michael I.},
+ date = {2022-09-03},
+ title = {Uncertainty {{Sets}} for {{Image Classifiers}} Using {{Conformal Prediction}}},
+ eprint = {2009.14193},
+ eprinttype = {arxiv},
+ url = {http://arxiv.org/abs/2009.14193},
+ urldate = {2022-12-07},
+ 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},
+ number = {arXiv:2009.14193},
+ primaryclass = {cs, math, stat},
+ publisher = {{arXiv}},
+}
+
+@article{angelucci2009indirect,
+ author = {Angelucci, Manuela and De Giorgi, Giacomo},
+ date = {2009},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {American economic review},
+ number = {1},
+ pages = {486--508},
+ title = {Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?},
+ volume = {99}}
+
+@article{angrist1990lifetime,
+ author = {Angrist, Joshua D},
+ date = {1990},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The American Economic Review},
+ pages = {313--336},
+ title = {Lifetime Earnings and the {{Vietnam}} Era Draft Lottery: Evidence from Social Security Administrative Records}}
+
+@unpublished{antoran2020getting,
+ archiveprefix = {arXiv},
+ author = {Antor{\'a}n, Javier and Bhatt, Umang and Adel, Tameem and Weller, Adrian and Hern{\'a}ndez-Lobato, Jos{\'e} Miguel},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2006.06848},
+ eprinttype = {arxiv},
+ title = {Getting a Clue: {{A}} Method for Explaining Uncertainty Estimates}}
+
+@article{arcones1992bootstrap,
+ author = {Arcones, Miguel A and Gine, Evarist},
+ date = {1992},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The Annals of Statistics},
+ pages = {655--674},
+ title = {On the Bootstrap of {{U}} and {{V}} Statistics}}
+
+@article{ariely2003coherent,
+ author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+ date = {2003},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The Quarterly journal of economics},
+ number = {1},
+ pages = {73--106},
+ title = {``{{Coherent}} Arbitrariness'': {{Stable}} Demand Curves without Stable Preferences},
+ volume = {118}}
+
+@article{ariely2006tom,
+ author = {Ariely, Dan and Loewenstein, George and Prelec, Drazen},
+ date = {2006},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Economic Behavior \& Organization},
+ number = {1},
+ pages = {1--10},
+ title = {Tom {{Sawyer}} and the Construction of Value},
+ volume = {60}}
+
+@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},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Information Fusion},
+ pages = {82--115},
+ title = {Explainable {{Artificial Intelligence}} ({{XAI}}): {{Concepts}}, Taxonomies, Opportunities and Challenges toward Responsible {{AI}}},
+ volume = {58}}
+
+@article{auer2002finitetime,
+ author = {Auer, Peter and Cesa-Bianchi, Nicolo and Fischer, Paul},
+ date = {2002},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Machine learning},
+ number = {2},
+ pages = {235--256},
+ title = {Finite-Time Analysis of the Multiarmed Bandit Problem},
+ volume = {47}}
+
+@article{barabasi2016network,
+ author = {Barab{\'a}si, Albert-L{\'a}szl{\'o}},
+ date = {2016},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Network Science},
+ title = {Network {{Science}}}}
+
+@unpublished{bastounis2021mathematics,
+ archiveprefix = {arXiv},
+ author = {Bastounis, Alexander and Hansen, Anders C and Vla{\v c}i{\'c}, Verner},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2109.06098},
+ eprinttype = {arxiv},
+ title = {The Mathematics of Adversarial Attacks in {{AI}}--{{Why}} Deep Learning Is Unstable despite the Existence of Stable Neural Networks}}
+
+@article{bechara1997deciding,
+ author = {Bechara, Antoine and Damasio, Hanna and Tranel, Daniel and Damasio, Antonio R},
+ date = {1997},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Science (New York, N.Y.)},
+ number = {5304},
+ pages = {1293--1295},
+ shortjournal = {Science},
+ title = {Deciding Advantageously before Knowing the Advantageous Strategy},
+ volume = {275}}
+
+@book{berlinet2011reproducing,
+ author = {Berlinet, Alain and Thomas-Agnan, Christine},
+ date = {2011},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Springer Science \& Business Media}},
+ title = {Reproducing Kernel {{Hilbert}} Spaces in Probability and Statistics}}
+
+@misc{bernanke1990federal,
+ author = {Bernanke, Ben S},
+ date = {1990},
+ 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}},
+ title = {The Federal Funds Rate and the Channels of Monetary Transnission}}
+
+@article{besbes2014stochastic,
+ author = {Besbes, Omar and Gur, Yonatan and Zeevi, Assaf},
+ date = {2014},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Advances in neural information processing systems},
+ pages = {199--207},
+ title = {Stochastic Multi-Armed-Bandit Problem with Non-Stationary Rewards},
+ volume = {27}}
+
+@article{bholat2020impact,
+ author = {Bholat, D and Gharbawi, M and Thew, O},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Bank of England Quarterly Bulletin, Q4},
+ title = {The {{Impact}} of {{Covid}} on {{Machine Learning}} and {{Data Science}} in {{UK Banking}}}}
+
+@book{bishop2006pattern,
+ author = {Bishop, Christopher M},
+ date = {2006},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{springer}},
+ title = {Pattern Recognition and Machine Learning}}
+
+@article{blaom2020mlj,
+ 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},
+ author = {Blaom, Anthony D. and Kiraly, Franz and Lienart, Thibaut and Simillides, Yiannis and Arenas, Diego and Vollmer, Sebastian J.},
+ date = {2020-11-07},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ doi = {10.21105/joss.02704},
+ 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},
+ issn = {2475-9066},
+ journaltitle = {Journal of Open Source Software},
+ langid = {english},
+ number = {55},
+ pages = {2704},
+ shorttitle = {{{MLJ}}},
+ title = {{{MLJ}}: {{A Julia}} Package for Composable Machine Learning},
+ url = {https://joss.theoj.org/papers/10.21105/joss.02704},
+ urldate = {2022-10-27},
+ volume = {5},
+ bdsk-url-1 = {https://joss.theoj.org/papers/10.21105/joss.02704},
+ bdsk-url-2 = {https://doi.org/10.21105/joss.02704}}
+
+@inproceedings{blundell2015weight,
+ author = {Blundell, Charles and Cornebise, Julien and Kavukcuoglu, Koray and Wierstra, Daan},
+ booktitle = {International Conference on Machine Learning},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {1613--1622},
+ publisher = {{PMLR}},
+ title = {Weight Uncertainty in Neural Network}}
+
+@article{borch2022machine,
+ author = {Borch, Christian},
+ date = {2022},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Technology in Society},
+ pages = {101852},
+ title = {Machine Learning, Knowledge Risk, and Principal-Agent Problems in Automated Trading}}
+
+@unpublished{borisov2021deep,
+ archiveprefix = {arXiv},
+ author = {Borisov, Vadim and Leemann, Tobias and Se{\ss}ler, Kathrin and Haug, Johannes and Pawelczyk, Martin and Kasneci, Gjergji},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2110.01889},
+ eprinttype = {arxiv},
+ title = {Deep Neural Networks and Tabular Data: {{A}} Survey}}
+
+@article{bramoulle2009identification,
+ author = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+ date = {2009},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of econometrics},
+ number = {1},
+ pages = {41--55},
+ title = {Identification of Peer Effects through Social Networks},
+ volume = {150}}
+
+@article{bramoulle2020peer,
+ author = {Bramoull{\'e}, Yann and Djebbari, Habiba and Fortin, Bernard},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Annual Review of Economics},
+ pages = {603--629},
+ title = {Peer Effects in Networks: {{A}} Survey},
+ volume = {12}}
+
+@unpublished{branco2015survey,
+ archiveprefix = {arXiv},
+ author = {Branco, Paula and Torgo, Luis and Ribeiro, Rita},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1505.01658},
+ eprinttype = {arxiv},
+ title = {A Survey of Predictive Modelling under Imbalanced Distributions}}
+
+@book{brock1991nonlinear,
+ author = {Brock, William Allen and Brock, William A and Hsieh, David Arthur and LeBaron, Blake Dean and Brock, William E},
+ date = {1991},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{MIT press}},
+ title = {Nonlinear Dynamics, Chaos, and Instability: Statistical Theory and Economic Evidence}}
+
+@inproceedings{buolamwini2018gender,
+ author = {Buolamwini, Joy and Gebru, Timnit},
+ booktitle = {Conference on Fairness, Accountability and Transparency},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {77--91},
+ publisher = {{PMLR}},
+ title = {Gender Shades: {{Intersectional}} Accuracy Disparities in Commercial Gender Classification}}
+
+@unpublished{bussmann2020neural,
+ archiveprefix = {arXiv},
+ author = {Bussmann, Bart and Nys, Jannes and Latr{\'e}, Steven},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2010.09429},
+ eprinttype = {arxiv},
+ title = {Neural {{Additive Vector Autoregression Models}} for {{Causal Discovery}} in {{Time Series Data}}}}
+
+@report{card1993minimum,
+ author = {Card, David and Krueger, Alan B},
+ date = {1993},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ institution = {{National Bureau of Economic Research}},
+ title = {Minimum Wages and Employment: {{A}} Case Study of the Fast Food Industry in {{New Jersey}} and {{Pennsylvania}}}}
+
+@inproceedings{carlini2017evaluating,
+ author = {Carlini, Nicholas and Wagner, David},
+ booktitle = {2017 Ieee Symposium on Security and Privacy (Sp)},
+ date = {2017},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {39--57},
+ publisher = {{IEEE}},
+ title = {Towards Evaluating the Robustness of Neural Networks}}
+
+@article{carlisle2019racist,
+ author = {Carlisle, M.},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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}}
+
+@article{carrell2009does,
+ author = {Carrell, Scott E and Fullerton, Richard L and West, James E},
+ date = {2009},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Labor Economics},
+ number = {3},
+ pages = {439--464},
+ title = {Does Your Cohort Matter? {{Measuring}} Peer Effects in College Achievement},
+ volume = {27}}
+
+@article{carrell2013natural,
+ author = {Carrell, Scott E and Sacerdote, Bruce I and West, James E},
+ date = {2013},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Econometrica : journal of the Econometric Society},
+ number = {3},
+ pages = {855--882},
+ shortjournal = {Econometrica},
+ title = {From Natural Variation to Optimal Policy? {{The}} Importance of Endogenous Peer Group Formation},
+ volume = {81}}
+
+@article{carrizosa2021generating,
+ author = {Carrizosa, Emilio and Ramırez-Ayerbe, Jasone and Romero, Dolores},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ title = {Generating {{Collective Counterfactual Explanations}} in {{Score-Based Classification}} via {{Mathematical Optimization}}}}
+
+@article{cascarino2022explainable,
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+ date = {2022},
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+ title = {Explainable {{Artificial Intelligence}}: Interpreting Default Forecasting Models Based on {{Machine Learning}}}}
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+ archiveprefix = {arXiv},
+ author = {Hoff, Peter},
+ date = {2021},
+ 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},
+ title = {Bayes-Optimal Prediction with Frequentist Coverage Control}}
+
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+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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)}}
+
+@online{hoffmanGermanCreditData1994,
+ author = {Hoffman, Hans},
+ date = {1994},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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)}}
+
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+ eprint = {1811.01457},
+ eprinttype = {arxiv},
+ title = {Fashionable Modelling with Flux}}
+
+@article{innes2018flux,
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+ pages = {1617--1626},
+ publisher = {{PMLR}},
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+ title = {Forecasting Uk Inflation Bottom Up}}
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+ archiveprefix = {arXiv},
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+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1907.09615},
+ eprinttype = {arxiv},
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+
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+
+@article{markle2018goals,
+ author = {Markle, Alex and Wu, George and White, Rebecca and Sackett, Aaron},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Risk and Uncertainty},
+ number = {1},
+ pages = {19--50},
+ title = {Goals as Reference Points in Marathon Running: {{A}} Novel Test of Reference Dependence},
+ volume = {56}}
+
+@article{masini2021machine,
+ author = {Masini, Ricardo P and Medeiros, Marcelo C and Mendes, Eduardo F},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Economic Surveys},
+ title = {Machine Learning Advances for Time Series Forecasting}}
+
+@article{mccracken2016fredmd,
+ author = {McCracken, Michael W and Ng, Serena},
+ date = {2016},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Business \& Economic Statistics},
+ number = {4},
+ pages = {574--589},
+ title = {{{FRED-MD}}: {{A}} Monthly Database for Macroeconomic Research},
+ volume = {34}}
+
+@article{mcculloch1990logical,
+ author = {McCulloch, Warren S and Pitts, Walter},
+ date = {1990},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Bulletin of mathematical biology},
+ number = {1},
+ pages = {99--115},
+ title = {A Logical Calculus of the Ideas Immanent in Nervous Activity},
+ volume = {52}}
+
+@article{migut2015visualizing,
+ author = {Migut, MA and Worring, Marcel and Veenman, Cor J},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Data Mining and Knowledge Discovery},
+ number = {1},
+ pages = {273--295},
+ title = {Visualizing Multi-Dimensional Decision Boundaries in {{2D}}},
+ volume = {29}}
+
+@article{miller2019explanation,
+ author = {Miller, Tim},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Artificial intelligence},
+ pages = {1--38},
+ title = {Explanation in Artificial Intelligence: {{Insights}} from the Social Sciences},
+ volume = {267}}
+
+@inproceedings{miller2020strategic,
+ 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.},
+ author = {Miller, John and Milli, Smitha and Hardt, Moritz},
+ booktitle = {Proceedings of the 37th {{International Conference}} on {{Machine Learning}}},
+ date = {2020-11-21},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eventtitle = {International {{Conference}} on {{Machine Learning}}},
+ 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},
+ pages = {6917--6926},
+ publisher = {{PMLR}},
+ title = {Strategic {{Classification}} Is {{Causal Modeling}} in {{Disguise}}},
+ url = {https://proceedings.mlr.press/v119/miller20b.html},
+ urldate = {2022-11-03},
+ bdsk-url-1 = {https://proceedings.mlr.press/v119/miller20b.html}}
+
+@article{mischel1988nature,
+ author = {Mischel, Walter and Shoda, Yuichi and Peake, Philip K},
+ date = {1988},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of personality and social psychology},
+ number = {4},
+ pages = {687},
+ title = {The Nature of Adolescent Competencies Predicted by Preschool Delay of Gratification.},
+ volume = {54}}
+
+@inproceedings{mittelstadt2019explaining,
+ author = {Mittelstadt, Brent and Russell, Chris and Wachter, Sandra},
+ booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {279--288},
+ title = {Explaining Explanations in {{AI}}}}
+
+@book{molnar2020interpretable,
+ author = {Molnar, Christoph},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Lulu. com}},
+ title = {Interpretable Machine Learning}}
+
+@book{morgan2015counterfactuals,
+ author = {Morgan, Stephen L and Winship, Christopher},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Cambridge University Press}},
+ title = {Counterfactuals and Causal Inference}}
+
+@article{mosteller1951experimental,
+ author = {Mosteller, Frederick and Nogee, Philip},
+ date = {1951},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Political Economy},
+ number = {5},
+ pages = {371--404},
+ title = {An Experimental Measurement of Utility},
+ volume = {59}}
+
+@inproceedings{mothilal2020explaining,
+ author = {Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
+ booktitle = {Proceedings of the 2020 {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {607--617},
+ title = {Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations}}
+
+@book{murphy2012machine,
+ author = {Murphy, Kevin P},
+ date = {2012},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{MIT press}},
+ title = {Machine Learning: A Probabilistic Perspective}}
+
+@book{murphy2012machine,
+ author = {Murphy, Kevin P},
+ date = {2012},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{MIT press}},
+ title = {Machine Learning: {{A}} Probabilistic Perspective}}
+
+@book{murphy2022probabilistic,
+ author = {Murphy, Kevin P},
+ date = {2022},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{MIT Press}},
+ title = {Probabilistic {{Machine Learning}}: {{An}} Introduction}}
+
+@article{nagel1995unraveling,
+ author = {Nagel, Rosemarie},
+ date = {1995},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The American Economic Review},
+ number = {5},
+ pages = {1313--1326},
+ title = {Unraveling in Guessing Games: {{An}} Experimental Study},
+ volume = {85}}
+
+@unpublished{navarro-martinez2021bridging,
+ author = {Navarro-Martinez, Daniel and Wang, Xinghua},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ title = {Bridging the Gap between the Lab and the Field: {{Dictator}} Games and Donations}}
+
+@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}})},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {1--8},
+ publisher = {{IEEE}},
+ title = {Evaluating Model Drift in Machine Learning Algorithms}}
+
+@book{nocedal2006numerical,
+ author = {Nocedal, Jorge and Wright, Stephen},
+ date = {2006},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Springer Science \& Business Media}},
+ title = {Numerical Optimization}}
+
+@misc{oecd2021artificial,
+ author = {{OECD}},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ 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}}
+
+@online{oecdArtificialIntelligenceMachine2021,
+ author = {{OECD}},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{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}}
+
+@book{oneil2016weapons,
+ author = {O'Neil, Cathy},
+ date = {2016},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Crown}},
+ title = {Weapons of Math Destruction: {{How}} Big Data Increases Inequality and Threatens Democracy}}
+
+@article{pace1997sparse,
+ author = {Pace, R Kelley and Barry, Ronald},
+ date = {1997},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Statistics \& Probability Letters},
+ number = {3},
+ pages = {291--297},
+ title = {Sparse Spatial Autoregressions},
+ volume = {33}}
+
+@unpublished{parr2018matrix,
+ archiveprefix = {arXiv},
+ author = {Parr, Terence and Howard, Jeremy},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1802.01528},
+ eprinttype = {arxiv},
+ title = {The Matrix Calculus You Need for Deep Learning}}
+
+@unpublished{pawelczyk2021carla,
+ archiveprefix = {arXiv},
+ author = {Pawelczyk, Martin and Bielawski, Sascha and van den Heuvel, Johannes and Richter, Tobias and Kasneci, Gjergji},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2108.00783},
+ eprinttype = {arxiv},
+ title = {Carla: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms}}
+
+@book{pearl2018book,
+ author = {Pearl, Judea and Mackenzie, Dana},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Basic books}},
+ title = {The Book of Why: The New Science of Cause and Effect}}
+
+@article{pearl2019seven,
+ author = {Pearl, Judea},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Communications of the ACM},
+ number = {3},
+ pages = {54--60},
+ title = {The Seven Tools of Causal Inference, with Reflections on Machine Learning},
+ volume = {62}}
+
+@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},
+ date = {2011},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {the Journal of machine Learning research},
+ pages = {2825--2830},
+ title = {Scikit-Learn: {{Machine}} Learning in {{Python}}},
+ volume = {12}}
+
+@book{perry2010economic,
+ author = {Perry, George L and Tobin, James},
+ date = {2010},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Brookings Institution Press}},
+ title = {Economic {{Events}}, {{Ideas}}, and {{Policies}}: The 1960s and After}}
+
+@article{pfaff2008var,
+ author = {Pfaff, Bernhard and others},
+ date = {2008},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Statistical Software},
+ number = {4},
+ pages = {1--32},
+ title = {{{VAR}}, {{SVAR}} and {{SVEC}} Models: {{Implementation}} within {{R}} Package Vars},
+ volume = {27}}
+
+@book{pindyck2014microeconomics,
+ author = {Pindyck, Robert S and Rubinfeld, Daniel L},
+ date = {2014},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Pearson Education}},
+ title = {Microeconomics}}
+
+@article{pope2011numbers,
+ author = {Pope, Devin and Simonsohn, Uri},
+ date = {2011},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Psychological science},
+ number = {1},
+ pages = {71--79},
+ title = {Round Numbers as Goals: {{Evidence}} from Baseball, {{SAT}} Takers, and the Lab},
+ volume = {22}}
+
+@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}}},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {344--350},
+ title = {{{FACE}}: {{Feasible}} and Actionable Counterfactual Explanations}}
+
+@article{qu2015estimating,
+ author = {Qu, Xi and Lee, Lung-fei},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of Econometrics},
+ number = {2},
+ pages = {209--232},
+ title = {Estimating a Spatial Autoregressive Model with an Endogenous Spatial Weight Matrix},
+ volume = {184}}
+
+@article{rabanser2019failing,
+ author = {Rabanser, Stephan and G{\"u}nnemann, Stephan and Lipton, Zachary},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Advances in Neural Information Processing Systems},
+ title = {Failing Loudly: {{An}} Empirical Study of Methods for Detecting Dataset Shift},
+ volume = {32}}
+
+@unpublished{raghunathan2019adversarial,
+ archiveprefix = {arXiv},
+ author = {Raghunathan, Aditi and Xie, Sang Michael and Yang, Fanny and Duchi, John C and Liang, Percy},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1906.06032},
+ eprinttype = {arxiv},
+ title = {Adversarial Training Can Hurt Generalization}}
+
+@unpublished{raj2017taming,
+ archiveprefix = {arXiv},
+ author = {Raj, Vishnu and Kalyani, Sheetal},
+ date = {2017},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1707.09727},
+ eprinttype = {arxiv},
+ title = {Taming Non-Stationary Bandits: {{A Bayesian}} Approach}}
+
+@inproceedings{rasmussen2003gaussian,
+ author = {Rasmussen, Carl Edward},
+ booktitle = {Summer School on Machine Learning},
+ date = {2003},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {63--71},
+ publisher = {{Springer}},
+ title = {Gaussian Processes in Machine Learning}}
+
+@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},
+ date = {2016},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {1135--1144},
+ title = {"{{Why}} Should i Trust You?" {{Explaining}} the Predictions of Any Classifier}}
+
+@article{romer1989does,
+ author = {Romer, Christina D and Romer, David H},
+ date = {1989},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {NBER macroeconomics annual},
+ pages = {121--170},
+ title = {Does Monetary Policy Matter? {{A}} New Test in the Spirit of {{Friedman}} and {{Schwartz}}},
+ volume = {4}}
+
+@article{rudin2019stop,
+ author = {Rudin, Cynthia},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Nature Machine Intelligence},
+ number = {5},
+ pages = {206--215},
+ title = {Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead},
+ volume = {1}}
+
+@article{sacerdote2001peer,
+ author = {Sacerdote, Bruce},
+ date = {2001},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The Quarterly journal of economics},
+ number = {2},
+ pages = {681--704},
+ title = {Peer Effects with Random Assignment: {{Results}} for {{Dartmouth}} Roommates},
+ volume = {116}}
+
+@article{sadinle2019least,
+ author = {Sadinle, Mauricio and Lei, Jing and Wasserman, Larry},
+ date = {2019},
+ 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},
+ journaltitle = {Journal of the American Statistical Association},
+ number = {525},
+ pages = {223--234},
+ publisher = {{Taylor \& Francis}},
+ title = {Least Ambiguous Set-Valued Classifiers with Bounded Error Levels},
+ volume = {114}}
+
+@inproceedings{satopaa2011finding,
+ author = {Satopaa, Ville and Albrecht, Jeannie and Irwin, David and Raghavan, Barath},
+ booktitle = {2011 31st International Conference on Distributed Computing Systems Workshops},
+ date = {2011},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {166--171},
+ publisher = {{IEEE}},
+ title = {Finding a" Kneedle" in a Haystack: {{Detecting}} Knee Points in System Behavior}}
+
+@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}}},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {1756--1764},
+ publisher = {{PMLR}},
+ title = {Generating {{Interpretable Counterfactual Explanations By Implicit Minimisation}} of {{Epistemic}} and {{Aleatoric Uncertainties}}}}
+
+@book{schutze2008introduction,
+ author = {Sch{\"u}tze, Hinrich and Manning, Christopher D and Raghavan, Prabhakar},
+ date = {2008},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Cambridge University Press Cambridge}},
+ title = {Introduction to Information Retrieval},
+ volume = {39}}
+
+@article{shafir1993reasonbased,
+ author = {Shafir, Eldar and Simonson, Itamar and Tversky, Amos},
+ date = {1993},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Cognition},
+ number = {1-2},
+ pages = {11--36},
+ title = {Reason-Based Choice},
+ volume = {49}}
+
+@article{simonson1989choice,
+ author = {Simonson, Itamar},
+ date = {1989},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of consumer research},
+ number = {2},
+ pages = {158--174},
+ title = {Choice Based on Reasons: {{The}} Case of Attraction and Compromise Effects},
+ volume = {16}}
+
+@article{sims1986are,
+ author = {Sims, Christopher A and others},
+ date = {1986},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ issue = {Win},
+ journaltitle = {Quarterly Review},
+ pages = {2--16},
+ title = {Are Forecasting Models Usable for Policy Analysis?},
+ volume = {10}}
+
+@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}}},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {180--186},
+ title = {Fooling Lime and Shap: {{Adversarial}} Attacks on Post Hoc Explanation Methods}}
+
+@article{slack2021counterfactual,
+ author = {Slack, Dylan and Hilgard, Anna and Lakkaraju, Himabindu and Singh, Sameer},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Advances in Neural Information Processing Systems},
+ title = {Counterfactual Explanations Can Be Manipulated},
+ volume = {34}}
+
+@article{slovic1974who,
+ author = {Slovic, Paul and Tversky, Amos},
+ date = {1974},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Behavioral science},
+ number = {6},
+ pages = {368--373},
+ title = {Who Accepts {{Savage}}'s Axiom?},
+ volume = {19}}
+
+@unpublished{spooner2021counterfactual,
+ archiveprefix = {arXiv},
+ author = {Spooner, Thomas and Dervovic, Danial and Long, Jason and Shepard, Jon and Chen, Jiahao and Magazzeni, Daniele},
+ date = {2021},
+ 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},
+ title = {Counterfactual {{Explanations}} for {{Arbitrary Regression Models}}}}
+
+@article{srivastava2014dropout,
+ author = {Srivastava, Nitish and Hinton, Geoffrey and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
+ date = {2014},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The journal of machine learning research},
+ number = {1},
+ pages = {1929--1958},
+ title = {Dropout: A Simple Way to Prevent Neural Networks from Overfitting},
+ volume = {15}}
+
+@unpublished{stanton2022bayesian,
+ archiveprefix = {arXiv},
+ author = {Stanton, Samuel and Maddox, Wesley and Wilson, Andrew Gordon},
+ date = {2022},
+ 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},
+ title = {Bayesian {{Optimization}} with {{Conformal Coverage Guarantees}}}}
+
+@article{sturm2014simple,
+ author = {Sturm, Bob L},
+ date = {2014},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {IEEE Transactions on Multimedia},
+ number = {6},
+ pages = {1636--1644},
+ title = {A Simple Method to Determine If a Music Information Retrieval System Is a ``Horse''},
+ volume = {16}}
+
+@article{sunstein2003libertarian,
+ author = {Sunstein, Cass R and Thaler, Richard H},
+ date = {2003},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {The University of Chicago Law Review},
+ pages = {1159--1202},
+ title = {Libertarian Paternalism Is Not an Oxymoron}}
+
+@book{sutton2018reinforcement,
+ author = {Sutton, Richard S and Barto, Andrew G},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{MIT press}},
+ title = {Reinforcement Learning: {{An}} Introduction}}
+
+@unpublished{szegedy2013intriguing,
+ archiveprefix = {arXiv},
+ author = {Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
+ date = {2013},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1312.6199},
+ eprinttype = {arxiv},
+ title = {Intriguing Properties of Neural Networks}}
+
+@article{thaler1981empirical,
+ author = {Thaler, Richard},
+ date = {1981},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Economics letters},
+ number = {3},
+ pages = {201--207},
+ title = {Some Empirical Evidence on Dynamic Inconsistency},
+ volume = {8}}
+
+@article{thaler2004more,
+ author = {Thaler, Richard H and Benartzi, Shlomo},
+ date = {2004},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of political Economy},
+ number = {S1},
+ pages = {S164--S187},
+ title = {Save More Tomorrow{\texttrademark}: {{Using}} Behavioral Economics to Increase Employee Saving},
+ volume = {112}}
+
+@article{tversky1981framing,
+ author = {Tversky, Amos and Kahneman, Daniel},
+ date = {1981},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Science (New York, N.Y.)},
+ number = {4481},
+ pages = {453--458},
+ shortjournal = {science},
+ title = {The Framing of Decisions and the Psychology of Choice},
+ volume = {211}}
+
+@article{ungemach2011how,
+ author = {Ungemach, Christoph and Stewart, Neil and Reimers, Stian},
+ date = {2011},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Psychological Science},
+ number = {2},
+ pages = {253--260},
+ title = {How Incidental Values from the Environment Affect Decisions about Money, Risk, and Delay},
+ volume = {22}}
+
+@unpublished{upadhyay2021robust,
+ archiveprefix = {arXiv},
+ author = {Upadhyay, Sohini and Joshi, Shalmali and Lakkaraju, Himabindu},
+ date = {2021},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2102.13620},
+ eprinttype = {arxiv},
+ title = {Towards {{Robust}} and {{Reliable Algorithmic Recourse}}}}
+
+@inproceedings{ustun2019actionable,
+ author = {Ustun, Berk and Spangher, Alexander and Liu, Yang},
+ booktitle = {Proceedings of the {{Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+ date = {2019},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ pages = {10--19},
+ title = {Actionable Recourse in Linear Classification}}
+
+@article{vanboven2000egocentric,
+ author = {Van Boven, Leaf and Dunning, David and Loewenstein, George},
+ date = {2000},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of personality and social psychology},
+ number = {1},
+ pages = {66},
+ title = {Egocentric Empathy Gaps between Owners and Buyers: Misperceptions of the Endowment Effect.},
+ volume = {79}}
+
+@book{varshney2022trustworthy,
+ author = {Varshney, Kush R.},
+ date = {2022},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ location = {{Chappaqua, NY, USA}},
+ publisher = {{Independently Published}},
+ title = {Trustworthy {{Machine Learning}}}}
+
+@unpublished{verma2020counterfactual,
+ archiveprefix = {arXiv},
+ author = {Verma, Sahil and Dickerson, John and Hines, Keegan},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2010.10596},
+ eprinttype = {arxiv},
+ title = {Counterfactual Explanations for Machine Learning: {{A}} Review}}
+
+@article{verstyuk2020modeling,
+ author = {Verstyuk, Sergiy},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Available at SSRN 3589337},
+ title = {Modeling Multivariate Time Series in Economics: {{From}} Auto-Regressions to Recurrent Neural Networks}}
+
+@article{wachter2017counterfactual,
+ author = {Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
+ date = {2017},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Harv. JL \& Tech.},
+ pages = {841},
+ title = {Counterfactual Explanations without Opening the Black Box: {{Automated}} Decisions and the {{GDPR}}},
+ volume = {31}}
+
+@article{wang2018optimal,
+ author = {Wang, HaiYing and Zhu, Rong and Ma, Ping},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Journal of the American Statistical Association},
+ number = {522},
+ pages = {829--844},
+ title = {Optimal Subsampling for Large Sample Logistic Regression},
+ volume = {113}}
+
+@book{wasserman2006all,
+ author = {Wasserman, Larry},
+ date = {2006},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Springer Science \& Business Media}},
+ title = {All of Nonparametric Statistics}}
+
+@book{wasserman2013all,
+ author = {Wasserman, Larry},
+ date = {2013},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ publisher = {{Springer Science \& Business Media}},
+ title = {All of Statistics: A Concise Course in Statistical Inference}}
+
+@article{widmer1996learning,
+ author = {Widmer, Gerhard and Kubat, Miroslav},
+ date = {1996},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Machine learning},
+ number = {1},
+ pages = {69--101},
+ title = {Learning in the Presence of Concept Drift and Hidden Contexts},
+ volume = {23}}
+
+@unpublished{wilson2020case,
+ archiveprefix = {arXiv},
+ author = {Wilson, Andrew Gordon},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {2001.10995},
+ eprinttype = {arxiv},
+ title = {The Case for {{Bayesian}} Deep Learning}}
+
+@article{witten2009penalized,
+ author = {Witten, Daniela M and Tibshirani, Robert and Hastie, Trevor},
+ date = {2009},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Biostatistics (Oxford, England)},
+ number = {3},
+ pages = {515--534},
+ shortjournal = {Biostatistics},
+ title = {A Penalized Matrix Decomposition, with Applications to Sparse Principal Components and Canonical Correlation Analysis},
+ volume = {10}}
+
+@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.},
+ date = {2020},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ doi = {doi.org/10.1038/s41597-020-0448-0},
+ journaltitle = {Scientific Data},
+ number = {106},
+ title = {Epidemiological Data from the {{COVID-19}} Outbreak, Real-Time Case Information},
+ volume = {7},
+ bdsk-url-1 = {https://doi.org/10.1038/s41597-020-0448-0}}
+
+@article{yeh2009comparisons,
+ author = {Yeh, I-Cheng and Lien, Che-hui},
+ date = {2009},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Expert systems with applications},
+ number = {2},
+ pages = {2473--2480},
+ title = {The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients},
+ volume = {36}}
+
+@article{zhang1998forecasting,
+ author = {Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y},
+ date = {1998},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {International journal of forecasting},
+ number = {1},
+ pages = {35--62},
+ title = {Forecasting with Artificial Neural Networks:: {{The}} State of the Art},
+ volume = {14}}
+
+@article{zhang2003time,
+ author = {Zhang, G Peter},
+ date = {2003},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {Neurocomputing},
+ pages = {159--175},
+ title = {Time Series Forecasting Using a Hybrid {{ARIMA}} and Neural Network Model},
+ volume = {50}}
+
+@unpublished{zheng2018dags,
+ archiveprefix = {arXiv},
+ author = {Zheng, Xun and Aragam, Bryon and Ravikumar, Pradeep and Xing, Eric P},
+ date = {2018},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ eprint = {1803.01422},
+ eprinttype = {arxiv},
+ title = {Dags with No Tears: {{Continuous}} Optimization for Structure Learning}}
+
+@article{zhu2015optimal,
+ author = {Zhu, Rong and Ma, Ping and Mahoney, Michael W and Yu, Bin},
+ date = {2015},
+ date-added = {2022-12-13 12:58:01 +0100},
+ date-modified = {2022-12-13 12:58:01 +0100},
+ journaltitle = {arXiv},
+ pages = {arXiv--1509},
+ title = {Optimal Subsampling Approaches for Large Sample Linear Regression}}
+
+@Article{barber2021predictive,
+ author = {Barber, Rina Foygel and Candès, Emmanuel J. and Ramdas, Aaditya and Tibshirani, Ryan J.},
+ date = {2021-02},
+ journaltitle = {The Annals of Statistics},
+ title = {Predictive inference with the jackknife+},
+ doi = {10.1214/20-AOS1965},
+ issn = {0090-5364, 2168-8966},
+ number = {1},
+ pages = {486--507},
+ url = {https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-1/Predictive-inference-with-the-jackknife/10.1214/20-AOS1965.full},
+ 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},
+ keywords = {62F40, 62G08, 62G09, conformal inference, cross-validation, distribution-free, jackknife, leave-one-out, stability},
+ publisher = {Institute of Mathematical Statistics},
+}
+
+@TechReport{chouldechova2018frontiers,
+ author = {Chouldechova, Alexandra and Roth, Aaron},
+ date = {2018-10},
+ institution = {arXiv},
+ title = {The {Frontiers} of {Fairness} in {Machine} {Learning}},
+ doi = {10.48550/arXiv.1810.08810},
+ note = {arXiv:1810.08810 [cs, stat] type: article},
+ url = {http://arxiv.org/abs/1810.08810},
+ urldate = {2022-12-23},
+ 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.},
+ 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},
+}
+
+@Article{pawelczyk2022probabilistically,
+ author = {Pawelczyk, Martin and Datta, Teresa and van-den-Heuvel, Johannes and Kasneci, Gjergji and Lakkaraju, Himabindu},
+ date = {2022},
+ journaltitle = {arXiv preprint arXiv:2203.06768},
+ 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},
+ shorttitle = {Probabilistically {Robust} {Recourse}},
+}
+
+@Comment{jabref-meta: databaseType:biblatex;}
diff --git a/build/dev/proposal.html b/build/dev/proposal.html
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+<link href="proposal_files/libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+
+
+</head>
+
+<body>
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+ <nav id="TOC" role="doc-toc" class="toc-active">
+ <h2 id="toc-title">Table of contents</h2>
+
+ <ul>
+ <li><a href="#you-may-not-like-it-but-this-is-what-counterfactuals-look-like" id="toc-you-may-not-like-it-but-this-is-what-counterfactuals-look-like" class="nav-link active" data-scroll-target="#you-may-not-like-it-but-this-is-what-counterfactuals-look-like">You may not like it, but this is what counterfactuals look like</a></li>
+ <li><a href="#introduction-to-conformal-prediction" id="toc-introduction-to-conformal-prediction" class="nav-link" data-scroll-target="#introduction-to-conformal-prediction">Introduction to Conformal Prediction</a>
+ <ul class="collapse">
+ <li><a href="#post-hoc" id="toc-post-hoc" class="nav-link" data-scroll-target="#post-hoc">Post-hoc</a></li>
+ <li><a href="#intrinsic-conformal-training-maybe" id="toc-intrinsic-conformal-training-maybe" class="nav-link" data-scroll-target="#intrinsic-conformal-training-maybe">Intrinsic — Conformal Training [MAYBE]</a></li>
+ </ul></li>
+ <li><a href="#conformal-counterfactuals" id="toc-conformal-counterfactuals" class="nav-link" data-scroll-target="#conformal-counterfactuals">Conformal Counterfactuals</a>
+ <ul class="collapse">
+ <li><a href="#research-questions" id="toc-research-questions" class="nav-link" data-scroll-target="#research-questions">Research Questions</a></li>
+ </ul></li>
+ <li><a href="#experiments" id="toc-experiments" class="nav-link" data-scroll-target="#experiments">Experiments</a></li>
+ <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
+ </ul>
+</nav>
+</div>
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Research Proposal</h1>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+
+
+
+ </div>
+
+
+</header>
+
+<section id="you-may-not-like-it-but-this-is-what-counterfactuals-look-like" class="level2">
+<h2 class="anchored" data-anchor-id="you-may-not-like-it-but-this-is-what-counterfactuals-look-like">You may not like it, but this is what counterfactuals look like</h2>
+<ul>
+<li>Show DiCE for weak MLP</li>
+<li>Show Latent for same weak MLP</li>
+<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="introduction-to-conformal-prediction" class="level2">
+<h2 class="anchored" data-anchor-id="introduction-to-conformal-prediction">Introduction to Conformal Prediction</h2>
+<ul>
+<li>distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification</li>
+</ul>
+<section id="post-hoc" class="level3">
+<h3 class="anchored" data-anchor-id="post-hoc">Post-hoc</h3>
+<ul>
+<li>Take any fitted model and turn it into a conformal model using calibration data.</li>
+</ul>
+</section>
+<section id="intrinsic-conformal-training-maybe" class="level3">
+<h3 class="anchored" data-anchor-id="intrinsic-conformal-training-maybe">Intrinsic — Conformal Training [MAYBE]</h3>
+<ul>
+<li>Model explicitly trained for conformal prediction.</li>
+</ul>
+</section>
+</section>
+<section id="conformal-counterfactuals" class="level2">
+<h2 class="anchored" data-anchor-id="conformal-counterfactuals">Conformal Counterfactuals</h2>
+<ul>
+<li>Realistic counterfactuals by minimizing predictive uncertainty <span class="citation" data-cites="schut2021generating">(<a href="#ref-schut2021generating" role="doc-biblioref">Schut et al. 2021</a>)</span>.</li>
+<li>Problem: restricted to Bayesian models.</li>
+<li>Solution: post-hoc predictive uncertainty quantification.</li>
+<li>Conformal prediction is instance-based. So is CE.</li>
+<li>Does the coverage guarantee carry over to counterfactuals?</li>
+</ul>
+<section id="research-questions" class="level3">
+<h3 class="anchored" data-anchor-id="research-questions">Research Questions</h3>
+<ul>
+<li>Is CP alone enough to ensure realistic counterfactuals?</li>
+<li>Do counterfactuals improve further as the models get better?</li>
+<li>Do counterfactuals get more realistic as coverage</li>
+<li>What happens as we vary coverage and setsize?</li>
+<li>What happens as we improve the model robustness?</li>
+<li>What happens as we improve the model’s ability to incorporate predictive uncertainty (deep ensemble, laplace)?</li>
+</ul>
+</section>
+</section>
+<section id="experiments" class="level2">
+<h2 class="anchored" data-anchor-id="experiments">Experiments</h2>
+<ul>
+<li>Maybe: conformalised Laplace</li>
+<li>Benchmarking:
+<ul>
+<li>add PROBE into the mix</li>
+<li>compare travel costs to domain shits.</li>
+</ul></li>
+</ul>
+</section>
+<section id="references" class="level2 unnumbered">
+
+
+</section>
+
+<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" role="doc-bibliography">
+<div id="ref-schut2021generating" class="csl-entry" role="doc-biblioentry">
+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>.â€</span> In <em>International <span>Conference</span> on <span>Artificial Intelligence</span> and <span>Statistics</span></em>, 1756–64. <span>PMLR</span>.
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diff --git a/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css b/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css
new file mode 100644
index 00000000..d1c139cc
--- /dev/null
+++ b/build/dev/proposal_files/libs/bootstrap/bootstrap.min.css
@@ -0,0 +1,10 @@
+@import"https://fonts.googleapis.com/css?family=Roboto";@font-face{font-family:JuliaMono-Light;src:url("https://cdn.jsdelivr.net/gh/cormullion/juliamono/webfonts/JuliaMono-Light.woff2")}@font-face{font-family:"Bangla MN";src:url("https://db.onlinewebfonts.com/c/3d37467c4a9c7c1188bc3e0b7aadfa0d?family=Bangla+MN")}/*!
+ * 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)
+ */:root{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-white: #ffffff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--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: #343a40;--bs-gray-900: #212529;--bs-default: #dee2e6;--bs-primary: #4063D8;--bs-secondary: #6c757d;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-default-rgb: 222, 226, 230;--bs-primary-rgb: 64, 99, 216;--bs-secondary-rgb: 108, 117, 125;--bs-success-rgb: 25, 135, 84;--bs-info-rgb: 13, 202, 240;--bs-warning-rgb: 255, 193, 7;--bs-danger-rgb: 220, 53, 69;--bs-light-rgb: 248, 249, 250;--bs-dark-rgb: 33, 37, 41;--bs-white-rgb: 255, 255, 255;--bs-black-rgb: 0, 0, 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[screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}body:not(.floating):not(.docked) .page-columns.toc-left .page-columns{display:grid;gap:0;grid-template-columns:[screen-start] 1.5em [screen-start-inset page-start page-start-inset body-start-outset body-start body-content-start] minmax(0px, 1fr) [body-content-end body-end body-end-outset page-end-inset page-end screen-end-inset] 1.5em [screen-end]}nav[role=doc-toc]{display:none}}body,.page-row-navigation{grid-template-rows:[page-top] 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e=t.getBoundingClientRect();return{top:e.top+window.pageYOffset,left:e.left+window.pageXOffset}},position:t=>({top:t.offsetTop,left:t.offsetLeft})},V={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode;for(;n&&n.nodeType===Node.ELEMENT_NODE&&3!==n.nodeType;)n.matches(e)&&i.push(n),n=n.parentNode;return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(", ");return this.find(e,t).filter((t=>!c(t)&&l(t)))}},K="carousel",X={interval:5e3,keyboard:!0,slide:!1,pause:"hover",wrap:!0,touch:!0},Y={interval:"(number|boolean)",keyboard:"boolean",slide:"(boolean|string)",pause:"(string|boolean)",wrap:"boolean",touch:"boolean"},Q="next",G="prev",Z="left",J="right",tt={ArrowLeft:J,ArrowRight:Z},et="slid.bs.carousel",it="active",nt=".active.carousel-item";class st extends B{constructor(t,e){super(t),this._items=null,this._interval=null,this._activeElement=null,this._isPaused=!1,this._isSliding=!1,this.touchTimeout=null,this.touchStartX=0,this.touchDeltaX=0,this._config=this._getConfig(e),this._indicatorsElement=V.findOne(".carousel-indicators",this._element),this._touchSupported="ontouchstart"in document.documentElement||navigator.maxTouchPoints>0,this._pointerEvent=Boolean(window.PointerEvent),this._addEventListeners()}static get Default(){return X}static get NAME(){return K}next(){this._slide(Q)}nextWhenVisible(){!document.hidden&&l(this._element)&&this.next()}prev(){this._slide(G)}pause(t){t||(this._isPaused=!0),V.findOne(".carousel-item-next, .carousel-item-prev",this._element)&&(s(this._element),this.cycle(!0)),clearInterval(this._interval),this._interval=null}cycle(t){t||(this._isPaused=!1),this._interval&&(clearInterval(this._interval),this._interval=null),this._config&&this._config.interval&&!this._isPaused&&(this._updateInterval(),this._interval=setInterval((document.visibilityState?this.nextWhenVisible:this.next).bind(this),this._config.interval))}to(t){this._activeElement=V.findOne(nt,this._element);const e=this._getItemIndex(this._activeElement);if(t>this._items.length-1||t<0)return;if(this._isSliding)return void j.one(this._element,et,(()=>this.to(t)));if(e===t)return this.pause(),void this.cycle();const i=t>e?Q:G;this._slide(i,this._items[t])}_getConfig(t){return t={...X,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(K,t,Y),t}_handleSwipe(){const t=Math.abs(this.touchDeltaX);if(t<=40)return;const e=t/this.touchDeltaX;this.touchDeltaX=0,e&&this._slide(e>0?J:Z)}_addEventListeners(){this._config.keyboard&&j.on(this._element,"keydown.bs.carousel",(t=>this._keydown(t))),"hover"===this._config.pause&&(j.on(this._element,"mouseenter.bs.carousel",(t=>this.pause(t))),j.on(this._element,"mouseleave.bs.carousel",(t=>this.cycle(t)))),this._config.touch&&this._touchSupported&&this._addTouchEventListeners()}_addTouchEventListeners(){const t=t=>this._pointerEvent&&("pen"===t.pointerType||"touch"===t.pointerType),e=e=>{t(e)?this.touchStartX=e.clientX:this._pointerEvent||(this.touchStartX=e.touches[0].clientX)},i=t=>{this.touchDeltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this.touchStartX},n=e=>{t(e)&&(this.touchDeltaX=e.clientX-this.touchStartX),this._handleSwipe(),"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((t=>this.cycle(t)),500+this._config.interval))};V.find(".carousel-item img",this._element).forEach((t=>{j.on(t,"dragstart.bs.carousel",(t=>t.preventDefault()))})),this._pointerEvent?(j.on(this._element,"pointerdown.bs.carousel",(t=>e(t))),j.on(this._element,"pointerup.bs.carousel",(t=>n(t))),this._element.classList.add("pointer-event")):(j.on(this._element,"touchstart.bs.carousel",(t=>e(t))),j.on(this._element,"touchmove.bs.carousel",(t=>i(t))),j.on(this._element,"touchend.bs.carousel",(t=>n(t))))}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=tt[t.key];e&&(t.preventDefault(),this._slide(e))}_getItemIndex(t){return this._items=t&&t.parentNode?V.find(".carousel-item",t.parentNode):[],this._items.indexOf(t)}_getItemByOrder(t,e){const i=t===Q;return v(this._items,e,i,this._config.wrap)}_triggerSlideEvent(t,e){const i=this._getItemIndex(t),n=this._getItemIndex(V.findOne(nt,this._element));return j.trigger(this._element,"slide.bs.carousel",{relatedTarget:t,direction:e,from:n,to:i})}_setActiveIndicatorElement(t){if(this._indicatorsElement){const e=V.findOne(".active",this._indicatorsElement);e.classList.remove(it),e.removeAttribute("aria-current");const i=V.find("[data-bs-target]",this._indicatorsElement);for(let e=0;e<i.length;e++)if(Number.parseInt(i[e].getAttribute("data-bs-slide-to"),10)===this._getItemIndex(t)){i[e].classList.add(it),i[e].setAttribute("aria-current","true");break}}}_updateInterval(){const t=this._activeElement||V.findOne(nt,this._element);if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);e?(this._config.defaultInterval=this._config.defaultInterval||this._config.interval,this._config.interval=e):this._config.interval=this._config.defaultInterval||this._config.interval}_slide(t,e){const i=this._directionToOrder(t),n=V.findOne(nt,this._element),s=this._getItemIndex(n),o=e||this._getItemByOrder(i,n),r=this._getItemIndex(o),a=Boolean(this._interval),l=i===Q,c=l?"carousel-item-start":"carousel-item-end",h=l?"carousel-item-next":"carousel-item-prev",d=this._orderToDirection(i);if(o&&o.classList.contains(it))return void(this._isSliding=!1);if(this._isSliding)return;if(this._triggerSlideEvent(o,d).defaultPrevented)return;if(!n||!o)return;this._isSliding=!0,a&&this.pause(),this._setActiveIndicatorElement(o),this._activeElement=o;const f=()=>{j.trigger(this._element,et,{relatedTarget:o,direction:d,from:s,to:r})};if(this._element.classList.contains("slide")){o.classList.add(h),u(o),n.classList.add(c),o.classList.add(c);const t=()=>{o.classList.remove(c,h),o.classList.add(it),n.classList.remove(it,h,c),this._isSliding=!1,setTimeout(f,0)};this._queueCallback(t,n,!0)}else n.classList.remove(it),o.classList.add(it),this._isSliding=!1,f();a&&this.cycle()}_directionToOrder(t){return[J,Z].includes(t)?m()?t===Z?G:Q:t===Z?Q:G:t}_orderToDirection(t){return[Q,G].includes(t)?m()?t===G?Z:J:t===G?J:Z:t}static carouselInterface(t,e){const i=st.getOrCreateInstance(t,e);let{_config:n}=i;"object"==typeof e&&(n={...n,...e});const s="string"==typeof e?e:n.slide;if("number"==typeof e)i.to(e);else if("string"==typeof s){if(void 0===i[s])throw new TypeError(`No method named "${s}"`);i[s]()}else n.interval&&n.ride&&(i.pause(),i.cycle())}static jQueryInterface(t){return this.each((function(){st.carouselInterface(this,t)}))}static dataApiClickHandler(t){const e=n(this);if(!e||!e.classList.contains("carousel"))return;const i={...U.getDataAttributes(e),...U.getDataAttributes(this)},s=this.getAttribute("data-bs-slide-to");s&&(i.interval=!1),st.carouselInterface(e,i),s&&st.getInstance(e).to(s),t.preventDefault()}}j.on(document,"click.bs.carousel.data-api","[data-bs-slide], [data-bs-slide-to]",st.dataApiClickHandler),j.on(window,"load.bs.carousel.data-api",(()=>{const t=V.find('[data-bs-ride="carousel"]');for(let e=0,i=t.length;e<i;e++)st.carouselInterface(t[e],st.getInstance(t[e]))})),g(st);const ot="collapse",rt={toggle:!0,parent:null},at={toggle:"boolean",parent:"(null|element)"},lt="show",ct="collapse",ht="collapsing",dt="collapsed",ut=":scope .collapse .collapse",ft='[data-bs-toggle="collapse"]';class pt extends B{constructor(t,e){super(t),this._isTransitioning=!1,this._config=this._getConfig(e),this._triggerArray=[];const n=V.find(ft);for(let t=0,e=n.length;t<e;t++){const e=n[t],s=i(e),o=V.find(s).filter((t=>t===this._element));null!==s&&o.length&&(this._selector=s,this._triggerArray.push(e))}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return rt}static get NAME(){return ot}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t,e=[];if(this._config.parent){const t=V.find(ut,this._config.parent);e=V.find(".collapse.show, .collapse.collapsing",this._config.parent).filter((e=>!t.includes(e)))}const i=V.findOne(this._selector);if(e.length){const n=e.find((t=>i!==t));if(t=n?pt.getInstance(n):null,t&&t._isTransitioning)return}if(j.trigger(this._element,"show.bs.collapse").defaultPrevented)return;e.forEach((e=>{i!==e&&pt.getOrCreateInstance(e,{toggle:!1}).hide(),t||H.set(e,"bs.collapse",null)}));const n=this._getDimension();this._element.classList.remove(ct),this._element.classList.add(ht),this._element.style[n]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const s=`scroll${n[0].toUpperCase()+n.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct,lt),this._element.style[n]="",j.trigger(this._element,"shown.bs.collapse")}),this._element,!0),this._element.style[n]=`${this._element[s]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(j.trigger(this._element,"hide.bs.collapse").defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,u(this._element),this._element.classList.add(ht),this._element.classList.remove(ct,lt);const e=this._triggerArray.length;for(let t=0;t<e;t++){const e=this._triggerArray[t],i=n(e);i&&!this._isShown(i)&&this._addAriaAndCollapsedClass([e],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(ht),this._element.classList.add(ct),j.trigger(this._element,"hidden.bs.collapse")}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(lt)}_getConfig(t){return(t={...rt,...U.getDataAttributes(this._element),...t}).toggle=Boolean(t.toggle),t.parent=r(t.parent),a(ot,t,at),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=V.find(ut,this._config.parent);V.find(ft,this._config.parent).filter((e=>!t.includes(e))).forEach((t=>{const e=n(t);e&&this._addAriaAndCollapsedClass([t],this._isShown(e))}))}_addAriaAndCollapsedClass(t,e){t.length&&t.forEach((t=>{e?t.classList.remove(dt):t.classList.add(dt),t.setAttribute("aria-expanded",e)}))}static jQueryInterface(t){return this.each((function(){const e={};"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1);const i=pt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}j.on(document,"click.bs.collapse.data-api",ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=i(this);V.find(e).forEach((t=>{pt.getOrCreateInstance(t,{toggle:!1}).toggle()}))})),g(pt);var mt="top",gt="bottom",_t="right",bt="left",vt="auto",yt=[mt,gt,_t,bt],wt="start",Et="end",At="clippingParents",Tt="viewport",Ot="popper",Ct="reference",kt=yt.reduce((function(t,e){return t.concat([e+"-"+wt,e+"-"+Et])}),[]),Lt=[].concat(yt,[vt]).reduce((function(t,e){return t.concat([e,e+"-"+wt,e+"-"+Et])}),[]),xt="beforeRead",Dt="read",St="afterRead",Nt="beforeMain",It="main",Pt="afterMain",jt="beforeWrite",Mt="write",Ht="afterWrite",Bt=[xt,Dt,St,Nt,It,Pt,jt,Mt,Ht];function Rt(t){return t?(t.nodeName||"").toLowerCase():null}function Wt(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function $t(t){return t instanceof Wt(t).Element||t instanceof Element}function zt(t){return t instanceof Wt(t).HTMLElement||t instanceof HTMLElement}function qt(t){return"undefined"!=typeof ShadowRoot&&(t instanceof Wt(t).ShadowRoot||t instanceof ShadowRoot)}const Ft={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];zt(s)&&Rt(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return Object.assign(e.elements.popper.style,i.popper),e.styles=i,e.elements.arrow&&Object.assign(e.elements.arrow.style,i.arrow),function(){Object.keys(e.elements).forEach((function(t){var n=e.elements[t],s=e.attributes[t]||{},o=Object.keys(e.styles.hasOwnProperty(t)?e.styles[t]:i[t]).reduce((function(t,e){return t[e]="",t}),{});zt(n)&&Rt(n)&&(Object.assign(n.style,o),Object.keys(s).forEach((function(t){n.removeAttribute(t)})))}))}},requires:["computeStyles"]};function Ut(t){return t.split("-")[0]}function Vt(t,e){var i=t.getBoundingClientRect();return{width:i.width/1,height:i.height/1,top:i.top/1,right:i.right/1,bottom:i.bottom/1,left:i.left/1,x:i.left/1,y:i.top/1}}function Kt(t){var e=Vt(t),i=t.offsetWidth,n=t.offsetHeight;return Math.abs(e.width-i)<=1&&(i=e.width),Math.abs(e.height-n)<=1&&(n=e.height),{x:t.offsetLeft,y:t.offsetTop,width:i,height:n}}function Xt(t,e){var i=e.getRootNode&&e.getRootNode();if(t.contains(e))return!0;if(i&&qt(i)){var 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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 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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 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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/build/dev/proposal_files/libs/clipboard/clipboard.min.js b/build/dev/proposal_files/libs/clipboard/clipboard.min.js
new file mode 100644
index 00000000..1103f811
--- /dev/null
+++ b/build/dev/proposal_files/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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diff --git a/build/dev/proposal_files/libs/quarto-html/anchor.min.js b/build/dev/proposal_files/libs/quarto-html/anchor.min.js
new file mode 100644
index 00000000..1c2b86fa
--- /dev/null
+++ b/build/dev/proposal_files/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,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) 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/build/dev/proposal_files/libs/quarto-html/popper.min.js b/build/dev/proposal_files/libs/quarto-html/popper.min.js
new file mode 100644
index 00000000..2269d669
--- /dev/null
+++ b/build/dev/proposal_files/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/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css b/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css
new file mode 100644
index 00000000..1efa3ee2
--- /dev/null
+++ b/build/dev/proposal_files/libs/quarto-html/quarto-syntax-highlighting.css
@@ -0,0 +1,171 @@
+/* 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: "JuliaMono-Light";
+}
+
+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;
+}
+
+code span.at {
+ color: #657422;
+}
+
+code span.ss {
+ color: #20794D;
+}
+
+code span.an {
+ color: #5E5E5E;
+}
+
+code span.fu {
+ color: #4758AB;
+}
+
+code span.st {
+ color: #20794D;
+}
+
+code span.cf {
+ color: #003B4F;
+}
+
+code span.op {
+ color: #5E5E5E;
+}
+
+code span.er {
+ color: #AD0000;
+}
+
+code span.bn {
+ color: #AD0000;
+}
+
+code span.al {
+ color: #AD0000;
+}
+
+code span.va {
+ color: #111111;
+}
+
+code span.pp {
+ color: #AD0000;
+}
+
+code span.in {
+ color: #5E5E5E;
+}
+
+code span.vs {
+ color: #20794D;
+}
+
+code span.wa {
+ color: #5E5E5E;
+ font-style: italic;
+}
+
+code span.do {
+ color: #5E5E5E;
+ font-style: italic;
+}
+
+code span.im {
+ color: #00769E;
+}
+
+code span.ch {
+ color: #20794D;
+}
+
+code span.dt {
+ color: #AD0000;
+}
+
+code span.fl {
+ color: #AD0000;
+}
+
+code span.co {
+ color: #5E5E5E;
+}
+
+code span.cv {
+ color: #5E5E5E;
+ font-style: italic;
+}
+
+code span.cn {
+ color: #8f5902;
+}
+
+code span.sc {
+ color: #5E5E5E;
+}
+
+code span.dv {
+ color: #AD0000;
+}
+
+code span.kw {
+ color: #003B4F;
+}
+
+.prevent-inlining {
+ content: "</";
+}
+
+/*# sourceMappingURL=debc5d5d77c3f9108843748ff7464032.css.map */
diff --git a/build/dev/proposal_files/libs/quarto-html/quarto.js b/build/dev/proposal_files/libs/quarto-html/quarto.js
new file mode 100644
index 00000000..8846a020
--- /dev/null
+++ b/build/dev/proposal_files/libs/quarto-html/quarto.js
@@ -0,0 +1,840 @@
+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`);
+ if (columnEl)
+ tabEl.addEventListener("shown.bs.tab", function (_event) {
+ 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);
+
+ // 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;
+
+ // 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 (depth === 1 || 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/build/dev/proposal_files/libs/quarto-html/tippy.css b/build/dev/proposal_files/libs/quarto-html/tippy.css
new file mode 100644
index 00000000..e6ae635c
--- /dev/null
+++ b/build/dev/proposal_files/libs/quarto-html/tippy.css
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js b/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js
new file mode 100644
index 00000000..ca292be3
--- /dev/null
+++ b/build/dev/proposal_files/libs/quarto-html/tippy.umd.min.js
@@ -0,0 +1,2 @@
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+
diff --git a/dev/proposal.qmd b/dev/proposal.qmd
new file mode 100644
index 00000000..8789e5a0
--- /dev/null
+++ b/dev/proposal.qmd
@@ -0,0 +1,63 @@
+---
+title: Conformal Counterfactual Explanations
+subtitle: Research Proposal
+abstract: |
+ 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 an effective way to produce plausible and model-agnostic counterfactual explanations, it not only introduces an significant engineering overhead, but also reallocates the task of creating realistic model explanations from the model itsel 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.
+---
+
+## Motivation
+
+Counterfactual Explanations are a powerful, flexible and intuitive way to not only explain black-box models, but also enable affected individuals to challenge them though the means of Algorithmic Recourse.
+
+### From Adversarial Examples to Counterfactual Explanations
+
+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 $x\in\mathcal{X}$ 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 is therefore little different from Adversarial Examples [@goodfellow2014explaining].
+
+> You may not like it, but this is what counterfactuals look like
+
+- Show DiCE for weak MLP
+- Show Latent for same weak MLP
+- Latent can be manipulated:
+ - train biased model
+ - train VAE with biased variable removed/attacked (use Boston housing dataset)
+ - hypothesis: will generate bias-free explanations
+
+## Introduction to Conformal Prediction
+
+- distribution-free, model-agnostic and scalable approach to predictive uncertainty quantification
+
+### Post-hoc
+
+- Take any fitted model and turn it into a conformal model using calibration data.
+
+### Intrinsic --- Conformal Training [MAYBE]
+
+- Model explicitly trained for conformal prediction.
+
+## Conformal Counterfactuals
+
+- Realistic counterfactuals by minimizing predictive uncertainty [@schut2021generating].
+- Problem: restricted to Bayesian models.
+- Solution: post-hoc predictive uncertainty quantification.
+- Conformal prediction is instance-based. So is CE.
+- Does the coverage guarantee carry over to counterfactuals?
+
+### Research Questions
+
+- Is CP alone enough to ensure realistic counterfactuals?
+- Do counterfactuals improve further as the models get better?
+- Do counterfactuals get more realistic as coverage
+- What happens as we vary coverage and setsize?
+- What happens as we improve the model robustness?
+- What happens as we improve the model's ability to incorporate predictive uncertainty (deep ensemble, laplace)?
+
+## Experiments
+
+- Maybe: conformalised Laplace
+- Benchmarking:
+ - add PROBE into the mix
+ - compare travel costs to domain shits.
+
+## References
+
+
--
GitLab