From 2bba74e0a10f89f695d1a7234d4241ef0a9ef682 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Thu, 2 Nov 2023 14:54:45 -0400 Subject: [PATCH 1/8] checkpointing --- zissou/_quarto.yml | 149 ++++ zissou/about.qmd | 45 + zissou/all.bib | 1760 +++++++++++++++++++++++++++++++++++++++ zissou/community.qmd | 10 + zissou/docs.qmd | 14 + zissou/img/logo_tm.png | Bin 0 -> 21065 bytes zissou/img/logo_tm.svg | 6 + zissou/img/numfocus.png | Bin 0 -> 13662 bytes zissou/index.qmd | 44 + zissou/install.qmd | 12 + zissou/styles.css | 80 ++ zissou/support.qmd | 21 + 12 files changed, 2141 insertions(+) create mode 100644 zissou/_quarto.yml create mode 100644 zissou/about.qmd create mode 100644 zissou/all.bib create mode 100644 zissou/community.qmd create mode 100644 zissou/docs.qmd create mode 100644 zissou/img/logo_tm.png create mode 100644 zissou/img/logo_tm.svg create mode 100644 zissou/img/numfocus.png create mode 100644 zissou/index.qmd create mode 100644 zissou/install.qmd create mode 100644 zissou/styles.css create mode 100644 zissou/support.qmd diff --git a/zissou/_quarto.yml b/zissou/_quarto.yml new file mode 100644 index 00000000..e37308e0 --- /dev/null +++ b/zissou/_quarto.yml @@ -0,0 +1,149 @@ +project: + type: website + +bibliography: all.bib + +website: + favicon: img/logo_tm.svg + title: "Stan" + image: "img/logo_tm.png" + reader-mode: true + + navbar: + logo: "img/logo_tm.png" + logo-alt: "Stan logo" + title: false + foreground: "#F2E5BD" + background: "#052744" + left: + - 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title: "Stan Users Guide" + style: "floating" + foreground: "#052744" + background: "#FEF8E8" + contents: + - docs/stan-users-guide/index.Rmd + - section: "Example Models" + contents: + - docs/stan-users-guide/regression.Rmd + - docs/stan-users-guide/time-series.Rmd + - docs/stan-users-guide/missing-data.Rmd + - text: " " + - section: "Programming Techniques" + contents: + - docs/stan-users-guide/floating-point.Rmd + - docs/stan-users-guide/matrices-arrays.Rmd + - docs/stan-users-guide/multi-indexing.Rmd + - text: " " + - section: "Posterior Inference & Model Checking" + contents: + - docs/stan-users-guide/posterior-prediction.Rmd + - docs/stan-users-guide/simulation-based-calibration.Rmd + - docs/stan-users-guide/posterior-predictive-checks.Rmd + - text: " " + - section: "Appendices" + contents: + - docs/stan-users-guide/using-stanc.Rmd + - docs/stan-users-guide/style-guide.Rmd + - docs/stan-users-guide/for-bugs-users.Rmd + + - title: "Stan Reference Manual" + contents: + - docs/reference-manual/index.Rmd + - section: "Language" + contents: + - docs/reference-manual/encoding.Rmd + - docs/reference-manual/includes.Rmd + - docs/reference-manual/comments.Rmd + - text: " " + - section: "Algorithms" + contents: + - docs/reference-manual/mcmc.Rmd + - docs/reference-manual/analysis.Rmd + - text: " " + - section: "Usage" + contents: + - docs/reference-manual/reproducibility.Rmd + - docs/reference-manual/licenses.Rmd + + - title: "Stan Functions Reference" + contents: + - docs/functions-reference/index.Rmd + - section: "Built-in Functions" + contents: + - docs/functions-reference/void_functions.Rmd + - docs/functions-reference/integer-valued_basic_functions.Rmd + - docs/functions-reference/real-valued_basic_functions.Rmd + - docs/functions-reference/complex-valued_basic_functions.Rmd + - text: " " + - section: "Discrete Distributions" + contents: + - docs/functions-reference/binary_distributions.Rmd + - docs/functions-reference/bounded_discrete_distributions.Rmd + - docs/functions-reference/unbounded_discrete_distributions.Rmd + - docs/functions-reference/multivariate_discrete_distributions.Rmd + - text: " " + - section: "Continuous Distributions" + contents: + - docs/functions-reference/unbounded_continuous_distributions.Rmd + - docs/functions-reference/positive_continuous_distributions.Rmd + - docs/functions-reference/positive_lower-bounded_distributions.Rmd + - text: " " + - section: "Additional Distributions" + contents: + - docs/functions-reference/hidden_markov_models.Rmd + - text: " " + - section: "Appendix" + contents: + - docs/functions-reference/mathematical_functions.Rmd + + + +format: + html: + theme: litera + css: styles.css + toc: true + fontfamily: sans + + diff --git a/zissou/about.qmd b/zissou/about.qmd new file mode 100644 index 00000000..00b7c8b7 --- /dev/null +++ b/zissou/about.qmd @@ -0,0 +1,45 @@ +Stan has grown from a 3-person research project started at Columbia University in 2011 to +a global community of developers, researchers, and users. + +## Open Code & Reproducible Science + +Stan is freedom-respecting, open-source software (new BSD core, some interfaces GPLv3).  Stan +is associated with NumFOCUS, a 501(c)(3) nonprofit supporting open code +and reproducible science, through which you can [help support +Stan](support.html#contribute-money). + +
+ +
+ +## Acknowledgements {-} + +The Stan project owes its success to the contributions from hundreds of +developers, researchers, active users, and funders. +Individual contributions to the software and documentation can be tracked through GitHub. +We are grateful to all the users who have taken the time to file bug reports and +feature requests via the GitHub and the Stan forums; this feedback has greatly +improved Stan's usability and reliability. + +Stan has been funded through grants for Stan and its developers, +through in-kind donations in the form of companies contributing +developer time to Stan and individuals contributing their own time to +Stan, and through donations to the open-source scientific software +non-profit NumFOCUS. + + +## Copyright and trademark {-} + +* Copyright 2011--2022, Stan Development Team and their assignees. + +* The Stan name and logo are registered trademarks of NumFOCUS. + + +## Licensing {-} + +* *Text content:* [CC-BY ND 4.0 license](https://creativecommons.org/licenses/by-nd/4.0/legalcode) + +* *Computer code:* [BSD 3-clause license](https://opensource.org/licenses/BSD-3-Clause) + +* *Logo:* [Stan logo usage guidelines](https://mc-stan.org/about/logo/) diff --git a/zissou/all.bib b/zissou/all.bib new file mode 100644 index 00000000..7d4ed81c --- /dev/null +++ b/zissou/all.bib @@ -0,0 +1,1760 @@ +% a do-nothing command that serves a purpose +@preamble{ " \newcommand{\noop}[1]{} " } + +@article{Wichura:1988, + URL = {http://www.jstor.org/stable/2347330}, + author = {Michael J. 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H. and Pereyra, V.}, + title = {The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate}, + journal = {SIAM Journal on Numerical Analysis}, + volume = {10}, + number = {2}, + pages = {413-432}, + year = {1973}, + doi = {10.1137/0710036}, + URL = {https://doi.org/10.1137/0710036}, + eprint = {https://doi.org/10.1137/0710036} +} + +@article{GneitingRaftery:2007, + author={Gneiting, Tilmann and Raftery, Adrian E}, + year={2007}, + title={Strictly proper scoring rules, prediction, and estimation}, + journal={Journal of the American Statistical Association}, + volume={102}, + number={477}, + pages={359--378} +} + +@article{BayarriBerger:2000, +Author = {Bayarri, MJ and Berger, James O}, +Journal = {Journal of the American Statistical Association}, +Number = {452}, +Pages = {1127--1142}, +Title = {P values for composite null models}, +Volume = {95}, +Year = {2000}} + +@article{GabryEtAl:2019, + author={Jonah Gabry and Aki Vehtari and M{\aa}ns Magnusson and + Yuling Yao and Andrew Gelman and Paul-Christian B\"urkner + and Ben Goodrich and Juho Piironen}, + year={2019}, + title={{l}oo: Efficient leave-one-out cross-validation and {WAIC} + for {B}ayesian models}, + journal={The Comprehensive {R} Network}, + volume={2}, + number={2} +} + +@article{VehtariEtAl:2017, + author = {Vehtari, Aki and Gelman, Andrew and Gabry, Jonah}, + year = {2017}, + title = {Practical {B}ayesian model evaluation using leave-one-out + cross-validation and {WAIC}}, + journal = {Statistics and computing}, + volume = {27}, + number = {5}, + pages = {1413--1432} +} + +@article{Rao:1945, + title = {Information and accuracy attainable in the estimation of statistical parameters}, + author = {Rao, C. Radhakrishna}, + journal = {Bulletin of the Calcutta Math Society}, + volume = {37}, + number = {3}, + pages = {81--91}, + year = {1945} +} + +@article{Blackwell:1947, + title = {Conditional expectation and unbiased sequential estimation}, + author = {Blackwell, David}, + journal = {The Annals of Mathematical Statistics}, + volume = {18}, + number = {1}, + pages = {105--110}, + year = {1947}, + doi = {10.1214/aoms/1177730497} +} + +@article{Rubin:1984, + title = {Bayesianly justifiable and relevant frequency calculations for the applied statistician}, + author = {Rubin, Donald B}, + journal = {The Annals of Statistics}, + pages = {1151--1172}, + year = {1984} +} + + +@article{GelmanEtAl:1996, + title = {Posterior predictive assessment of model fitness via + realized discrepancies}, + author = {Gelman, Andrew and Meng, Xiao-Li and Stern, Hal}, + journal = {Statistica Sinica}, + pages = {733--760}, + year = {1996} +} + +@article{Little:1993, + author={Little, Roderick JA}, + year={1993}, + title={Post-stratification: a modeler's perspective}, + journal={Journal of the American Statistical Association}, + volume={88}, + number={423}, + pages={1001--1012} +} + +@article{Kennedy:2019, + author={Kennedy, Lauren and Gelman, Andrew}, + year={2019}, + title={Know your population and know your model: Using model-based + regression and poststratification to generalize + findings beyond the observed sample}, + journal={arXiv}, + number = {1906.11323}, +} + +@article{Park:2004, + author={Park, David K and Gelman, Andrew and Bafumi, Joseph}, + year={2004}, + title={Bayesian multilevel estimation with poststratification: + State-level estimates from national polls}, + journal={Political Analysis}, + volume={12}, + number={4}, + pages={375--385} +} + +@article{Field:1998, + author={Field, Christopher B and Behrenfeld, Michael J and + Randerson, James T and Falkowski, Paul}, + year={1998}, + title={Primary production of the biosphere: integrating terrestrial + and oceanic 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inference and model criticism}, + author={Huggins, Jonathan H and Miller, Jeffrey W}, + journal={arXiv}, + number = {1912.07104}, + year={2019} +} + +@article{Dawid:1982, + title={The well-calibrated {B}ayesian}, + author={Dawid, A Philip}, + year={1982}, + journal={Journal of the American Statistical Association}, + volume={77}, + number={379}, + pages={605--610} +} + +@article{GneitingEtAl:2007, + title={Probabilistic forecasts, calibration and sharpness}, + author={Gneiting, Tilmann and Balabdaoui, Fadoua and Raftery, Adrian E}, + journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, + volume={69}, + number={2}, + pages={243--268}, + year={2007} +} + +@article{TaltsEtAl:2018, + title={Validating {B}ayesian inference algorithms with simulation-based calibration}, + author={Talts, Sean and Betancourt, Michael and Simpson, Daniel and Vehtari, Aki and Gelman, Andrew}, + year={2018}, + journal={arXiv}, + number = {1804.06788} +} + +@article{EfronTibshirani1986, + title={Bootstrap methods for standard errors, confidence intervals, + and other measures of statistical accuracy}, + author={Efron, Bradley and Tibshirani, Robert}, + journal={Statistical science}, + volume = {1}, + number = {1}, + pages={54--75}, + year={1986} +} + +@book{EfronTibshirani1994, + title={An introduction to the bootstrap}, + author={Efron, Bradley and Tibshirani, Robert J}, + year={1994}, + publisher={Chapman \& Hall/CRC} +} + +@book{Feller1968, + title={An Introduction to Probability Theory and its Applications}, + author={Feller, William}, + volume={1}, + number={3}, + year={1968}, + publisher={Wiley, New York} +} + +@article{NavarroFuss2009, + title={Fast and accurate calculations for first-passage times in {W}iener diffusion models}, + author={Navarro, Danielle J and Fuss, Ian G}, + journal={Journal of Mathematical Psychology}, + volume={53}, + number={4}, + pages={222--230}, + year={2009} +} + +@article{barnard-mcculloch-meng:2000, + title={Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage}, + author={Barnard, John and McCulloch, Robert and Meng, Xiao-Li}, + journal={Statistica Sinica}, + pages={1281--1311}, + year={2000}, +} + +@article{lancaster:2000, + title={The incidental parameter problem since 1948}, + author={Lancaster, Tony}, + journal={Journal of Econometrics}, + volume={95}, + number={2}, + pages={391--413}, + year={2000} +} + +@manual{minpack:1980, + Address = {9700 South Cass Avenue, Argonne, Illinois 60439}, + Author = {Jorge J. More, Burton S. Garbow, Kenneth E. Hillstrom}, + Month = {August}, + Organization = {Argonne National Laboratory}, + Title = {User Guide for MINPACK-1}, + Year = {1980} +} + +@incollection{Powell:1970, + Author = {Powell, Michael J. D.}, + Booktitle = {Numerical Methods for Nonlinear Algebraic Equations}, + Editor = {Rabinowitz, P.}, + Publisher = {Gordon and Breach}, + Title = {A Hybrid Method for Nonlinear Equations}, + Year = {1970} +} + +@inproceedings{PiironenVehtari:2016, + title={Projection predictive model selection for {Gaussian} processes}, + author={Piironen, Juho and Vehtari, Aki}, + booktitle={Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on}, + note = {doi:10.1109/MLSP.2016.7738829}, + year=2016, + organization={IEEE} +} + +@article{AhnertMulansky:2011, + author={Ahnert, Karsten and Mulansky, Mario}, + year={2011}, + title={Odeint---solving ordinary differential equations in {C}++}, + journal={arXiv}, + volume={1110.3397} +} + +@article{DormandPrince:1980, + author={Dormand, John R and Prince, Peter J}, + year={1980}, + title={A family of embedded {R}unge-{K}utta formulae}, + journal={Journal of Computational and Applied Mathematics}, + volume={6}, + number={1}, + pages={19--26} +} + +@article{CohenHindmarsh:1996, + author={Cohen, Scott D and Hindmarsh, Alan C}, + year={1996}, + title={{CVODE}, a stiff/nonstiff {ODE} solver in {C}}, + journal={Computers in Physics}, + volume={10}, + number={2}, + pages={138--143} +} + +@inproceedings{SerbanHindmarsh:2005, + author={Serban, Radu and Hindmarsh, Alan C}, + year={2005}, + title={{CVODES}: the sensitivity-enabled {ODE} solver in {SUNDIALS}}, + booktitle={ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, + pages={257--269}, + organization={American Society of Mechanical Engineers} +} + +@article{HoetingEtAl:1999, + title={Bayesian model averaging: a tutorial}, + author={Hoeting, Jennifer A. and Madigan, David + and Raftery, Adrian E and Volinsky, Chris T.}, + journal={Statistical Science}, + volume = {14}, + number = {4}, + pages={382--417}, + year={1999} +} + +@article{Marsaglia:1972, + title={Choosing a point from the surface of a sphere}, + author={Marsaglia, George}, + journal={The Annals of Mathematical Statistics}, + volume={43}, + number={2}, + pages={645--646}, + year={1972} +} + +@article{Muller:1959, + author = {Muller, Mervin E.}, + title = {A Note on a Method for Generating Points Uniformly on n-Dimensional Spheres}, + year = {1959}, + issue_date = {April 1959}, + publisher = {Association for Computing Machinery}, + address = {New York, NY, USA}, + volume = {2}, + number = {4}, + issn = {0001-0782}, + url = {https://doi.org/10.1145/377939.377946}, + doi = {10.1145/377939.377946}, + journal = {Commun. ACM}, + month = {apr}, + pages = {19–20}, + numpages = {2} +} + +@book{HopcroftMotwani:2006, + title={Introduction to Automata Theory, Languages, and Computation}, + author={Hopcroft, John E. and Rajeev Motwani}, + year={2006}, + edition={3rd}, + publisher={Pearson Education} +} + +@article{Church:1936, + title={An unsolvable problem of elementary number theory}, + author={Church, Alonzo}, + journal={American Journal of Mathematics}, + pages={345--363}, + year={1936} +} + +@article{turing:1936, + title={On computable numbers, with an application to the {E}ntscheidungsproblem}, + author={Turing, Alan Mathison}, + journal={J. of Math}, + volume={58}, + number={345-363}, + pages={5}, + year={1936} +} + +@article{Kucukelbir:2015, + title = {Automatic Variational Inference in {S}tan}, + author = {Alp Kucukelbir and Rajesh Ranganath and Andrew Gelman + and David M. Blei}, + year = {2015}, + journal = {arXiv}, + volume = {1506.03431}, + url = {http://arxiv.org/abs/1506.03431} +} + +@article{Jordan:1999, + Author = {Jordan, Michael I and Ghahramani, Zoubin and Jaakkola, Tommi S and Saul, Lawrence K}, + Journal = {Machine Learning}, + Number = {2}, + Pages = {183--233}, + Publisher = {Springer}, + Title = {An introduction to variational methods for graphical models}, + Volume = {37}, + Year = {1999} +} + +@article{Wainwright-Jordan:2008, + Author = {Wainwright, Martin J and Jordan, Michael I}, + Journal = {Foundations and Trends in Machine Learning}, + Number = {1-2}, + Pages = {1--305}, + Title = {Graphical models, exponential families, and variational inference}, + Volume = {1}, + Year = {2008} +} + +@article{Hoffman:2013, + Author = {Hoffman, Matthew D and Blei, David M and Wang, Chong and Paisley, John}, + Journal = {The Journal of Machine Learning Research}, + Number = {1}, + Pages = {1303--1347}, + Title = {Stochastic variational inference}, + Volume = {14}, + Year = {2013} +} + +@book{Bishop:2006, + Author = {Bishop, Christopher M}, + Publisher = {Springer New York}, + Title = {Pattern Recognition and Machine Learning}, + Year = {2006} +} + +@article{Duchi:2011, + Author = {Duchi, John and Hazan, Elad and Singer, Yoram}, + Journal = {The Journal of Machine Learning Research}, + Pages = {2121--2159}, + Title = {Adaptive subgradient methods for online learning and stochastic optimization}, + Volume = {12}, + Year = {2011}} + + +@article{Zellner:1962, + author = {Arnold Zellner}, + year = {1962}, + title = {An efficient method of estimating seemingly unrelated regression equations + and tests for aggregation bias}, + journal = {Journal of the American Statistical Association}, + volume={57}, + pages = {348--368} +} + +@book{Greene:2011, + author = {William H. Greene}, + year = {2011}, + title = {Econometric Analysis}, + edition = {7th}, + publisher = {Prentice-Hall} +} + +@techreport{CordenKreitzer:2014, + author = {Martyn J. Corden and David Kreitzer}, + year = {2014}, + title = {Consistency of Floating-Point Results using + the {I}ntel Compiler or {W}hy doesn't my application always + give the same answer?}, + institution = {Intel Corporation}, + url = {https://software.intel.com/en-us/articles/consistency-of-floating-point-results-using-the-intel-compiler} +} + +@article{LangfordEtAl:2009, + author = {John Langford and Lihong Li and Tong Zhang}, + year = {2009}, + title = {Sparse online learning via truncated gradient}, + journal = {Journal of Machine Learning Research}, + volume = {10}, + pages = {777--801} +} + +@book{LeeWagenmakers:2013, + author = {Michael D. Lee and Eric-Jan Wagenmakers}, + year = {2013}, + title = {Bayesian Cognitive Modeling: A Practical Course}, + publisher = {Cambridge University Press} +} + + +@book{Kruschke:2014, + author = {John Kruschke}, + year = {2014}, + title = {Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan}, + edition = {2nd}, + publisher = {Academic Press} +} + +@book{Chacon:2014, + author = {Scott Chacon and Ben Straub}, + year = {2014}, + title = {Pro Git}, + edition = {2nd}, + publisher = {Apress} +} + +@misc{Driessen:2010, + author = {Vincent Driessen}, + year = {2010}, + title = {A successful {G}it branching model}, + url = {http://nvie.com/posts/a-successful-git-branching-model/}, + note = {Online, accessed 6-January-2015} +} + +@techreport{RProject:2014, + author = {{R Project}}, + year = {2014}, + institution = {The R Foundation for Statistical Computing}, + title = {{R}: Regulatory Compliance and Validation Issues; + A Guidance Document for the Use of {R} in Regulated Clinical + Trial Environments}, + date = {December 15, 2014}, + note = {www.r-project.org/doc/R-FDA.pdf} +} + +@book{HastieTibshiraniFriedman:2009, + Author = {Trevor Hastie and Robert Tibshirani and Jerome Friedman}, + Year = {2009}, + Title = {The Elements of Statistical Learning: Data Mining, + Inference, and Prediction}, + Edition = {Second}, + Publisher = {Springer-Verlag}, + Address = {New York} +} + +@article{RobertsEtAl:1997, + Author = {Roberts, G.O. and Andrew Gelman and Walter R. Gilks}, + Year = {1997}, + Title = {Weak convergence and optimal scaling of random walk {M}etropolis algorithms}, + Journal = {Annals of Applied Probability}, + Volume = {7}, + number = {1}, + Pages = {110--120} +} + + +@article{ChungEtAl:2013, + author={Chung, Yeojin and Rabe-Hesketh, Sophia and Dorie, Vincent and Gelman, Andrew and Liu, Jingchen}, + title={A nondegenerate penalized likelihood estimator for variance parameters in multilevel models}, + year={2013}, + journal={Psychometrika}, + volume={78}, + number={4}, + pages={685--709} +} + +@article{GelmanJakulinPittauEtAl:2008, + author = {Andrew Gelman and Aleks Jakulin and Maria Grazia Pittau + and Yu-Sung Su}, + year = {2008}, + title = {A weakly informative default prior distribution for logistic + and other regression models}, + journal = {Annals of Applied Statistics}, + volume = {2}, + number = {4}, + pages = {1360--1383}} + + +@article{Gelman:2006, + Author = {Gelman, A.}, + Journal = {Bayesian Analysis}, + Number = {3}, + Pages = {515--534}, + Title = {Prior distributions for variance parameters in hierarchical models}, + Volume = {1}, + Year = {2006}} + +@book{LunnEtAl:2012, + author = {David Lunn and Christopher Jackson + and Nicky Best and Andrew Thomas + and David Spiegelhalter}, + year = {2012}, + title = {The {BUGS} Book: A Practical Introduction to {B}ayesian Analysis}, + publisher = {CRC Press/Chapman \& Hall} +} + +@article{EfronMorris:1975, + Author = {Efron, B. and Morris, C.}, + Journal = {Journal of the American Statistical Association}, + Pages = {311--319}, + Title = {Data analysis using Stein's estimator and its generalizations}, + Volume = {70}, + Year = {1975}} + +@book{NocedalWright:2006, + Author = { Jorge Nocedal and Stephen J.~Wright}, + Year = {2006}, + Publisher = {Springer-{V}erlag}, + Address = {Berlin}, + Title = {Numerical Optimization}, + Edition = {Second} +} + +@article{ZyczkowskiSommers:2001, + title={Induced measures in the space of mixed quantum states}, + author={Zyczkowski, K. and Sommers, H.J.}, + journal={Journal of Physics A: Mathematical and General}, + volume={34}, + number={35}, + pages={7111}, + year={2001}, + publisher={IOP Publishing} +} + +@article{aguilar-west:2000, + title={Bayesian dynamic factor models and portfolio allocation}, + author={Aguilar, Omar and West, Mike}, + journal={Journal of Business \& Economic Statistics}, + volume={18}, + number={3}, + pages={338--357}, + year={2000}, + publisher={Taylor \& Francis} +} + +@inproceedings{Airoldi:2009, + Author = {Airoldi, E. and Blei, D. and Fienberg, S. and Xing, E.}, + Booktitle = {Neural Information Processing Systems}, + Date-Added = {2008-12-20 13:05:43 -0500}, + Date-Modified = {2008-12-31 10:06:13 -0500}, + Keywords = {graphs}, + Title = {Mixed Membership Stochastic Blockmodels}, + Year = {2009} +} + +@inproceedings{Akaike:1973, + Address = {Budapest}, + Author = {Akaike, H.}, + Booktitle = {Proceedings of the Second International Symposium on Information Theory}, + Editor = {B. N. Petrov and F. Csaki}, + Note = {Reprinted in Breakthroughs in Statistics (pages 610--624), edited by S. Kotz, published by Springer-Verlag, New York in 1992}, + Pages = {267--281}, + Publisher = {Akademiai Kiado}, + Title = {Information theory and an extension of the maximum likelihood principle}, + Year = {1973} +} + +@article{AlbertChib:1993, + Author = {Albert, J. 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Epidemiology and Public Health, + Imperial College School of Medicine, London} +} + + +@book{McConnell:2004, + Author = {McConnell, Steve}, + Year = {2004}, + Title = {Code Complete: A Practical Handbook of Software Construction}, + Edition = {Second}, + Publisher = {Microsoft Press} +} + +@article{Metropolis:1953, + Author = {Metropolis, N. and Rosenbluth, A. and Rosenbluth, M. and Teller, M. and Teller, E.}, + Date-Modified = {2007-06-11 11:46:55 -0400}, + Journal = {Journal of Chemical Physics}, + Pages = {1087--1092}, + Title = {Equations of State Calculations by Fast Computing Machines}, + Volume = {21}, + Year = {1953} +} + +@article{MetropolisUlam:1949, + Author = {Metropolis, Nicholas and Ulam, Stanislaw}, + Title = {The {M}onte {C}arlo Method}, + Journal = {Journal of the American Statistical Association}, + Year = {1949}, + Volume = {44}, + Number = {247}, + Pages = {335--341} +} + +@article{Neal:1994, + Author = {Neal, Radford M.}, + Year = {1994}, + Title = {An improved acceptance procedure for the hybrid Monte Carlo algorithm}, + Journal = {Journal of Computational Physics}, + Volume = {111}, + Pages = {194--203} +} + +@article{Neal:1996b, + author = {Neal, Radford M.}, + year = {1996}, + title = {Sampling from multimodal distributions using tempered transitions}, + journal = {Statistics and Computing}, + volume = {6}, + number = {4}, + pages = {353--366} +} + +@techreport{Neal:1997, + author = {Neal, Radford M.}, + year = {1997}, + title = {Monte {C}arlo Implementation of {G}aussian Process Models for {B}ayesian Regression and Classification}, + institution = {University of Toronto, Department of Statistics}, + number = {9702} +} + +@article{Neal:2003, + Author = {Neal, Radford M.}, + Year = {2003}, + Title = {Slice Sampling}, + Journal = {Annals of Statistics}, + Volume = {31}, + Number = {3}, + Pages = {705--767} +} + +@incollection{Neal:2011, + Author = {Neal, Radford}, + Year = {2011}, + Title = {{MCMC} Using {H}amiltonian Dynamics}, + Booktitle = {Handbook of {M}arkov Chain {M}onte {C}arlo}, + Editor = {Brooks, Steve and Gelman, Andrew and Jones, Galin L. and Meng, Xiao-Li}, + Publisher = {Chapman and Hall/CRC}, + Pages = {116--162} +} + +@book{Neal:1996, + author = {Neal, Radford M.}, + year = {1996}, + title = {Bayesian Learning for Neural Networks}, + series = {Lecture Notes in Statistics}, + number = {118}, + publisher = {Springer}, + location = {New York} +} + +@article{Nesterov:2009, + title={Primal-dual subgradient methods for convex problems}, + author={Nesterov, Y.}, + journal={Mathematical Programming}, + volume={120}, + number={1}, + pages={221--259}, + year={2009}, + publisher={Springer} +} + +@article{numpy:2011, + author = {Walt, Stefan van der and Colbert, S.~Chris and Varoquaux, Gael}, + title = {The NumPy Array: A Structure for Efficient Numerical Computation}, + journal = {Computing in Science and Engg.}, + issue_date = {March 2011}, + volume = {13}, + issue = {2}, + month = {March}, + year = {2011}, + issn = {1521-9615}, + pages = {22--30}, + numpages = {9}, + url = {http://dx.doi.org/10.1109/MCSE.2011.37}, + doi = {http://dx.doi.org/10.1109/MCSE.2011.37}, + acmid = {1957466}, + publisher = {IEEE Educational Activities Department}, + address = {Piscataway, NJ, USA} +} + +@article{papa-et-al:2007, + author = {Papaspiliopoulos, Omiros and Roberts, Gareth O. and Sk{\"o}ld, Martin}, + year = {2007}, + title = {A General Framework for the Parametrization of Hierarchical Models}, + journal = {Statistical Science}, + volume = {22}, + number = {1}, + pages = {59--73} +} + +@article{Petersen:1896, + author = {C.~G.~J. Petersen}, + year = {1896}, + title ={The Yearly Immigration of Young Plaice Into the {L}imfjord From the {G}erman {S}ea}, + journal = {Report of the Danish Biological Station}, + volume = {6}, + pages = {5--84} +} + + +@article{PinheiroBates:1996, + author = {Pinheiro, Jos{\'e} C. and Bates, Douglas M.}, + title = {Unconstrained Parameterizations for Variance-Covariance Matrices}, + journal = {Statistics and Computing}, + year = {1996}, + volume = {6}, + pages = {289--296} +} + +@manual{PyMC:2014, + title = {PyMC User's Guide}, + author = {Chris Fonnesbeck + and Anand Patil + and David Huard + and John Salvatier}, + note = {Version 2.3}, + year = {2013} +} + + +@Manual{R:2014, + title = {R: A Language and Environment for Statistical Computing}, + author = {{R Development Core Team}}, + organization = {R Foundation for Statistical Computing}, + address = {Vienna, Austria}, + year = {2014}, + note = {{ISBN} 3-900051-07-0}, + url = {http://www.R-project.org}, +} + +@book{RasmussenWilliams:2006, + author={Rasmussen, Carl Edward and Williams, Christopher K.~I.}, + title={Gaussian Processes for Machine Learning}, + year={2006}, + publisher={MIT Press} +} + +@article{RichardsonGilks:1993, + author = {Richardson, Sylvia and Gilks, Walter R.}, + year = {1993}, + title = {A {B}ayesian approach to measurement error problems in epidemiology using conditional independence models}, + journal = {American Journal of Epidemiology}, + volume = {138}, + number = {6}, + pages = {430--442} +} + +@ARTICLE{Rnews:Plummer+Best+Cowles+Vines:2006, + AUTHOR = {Plummer, Martyn and Best, Nicky and Cowles, Kate and Vines, Karen}, + TITLE = {{CODA}: Convergence Diagnosis and Output Analysis for {MCMC}}, + JOURNAL = {R News}, + YEAR = 2006, + VOLUME = 6, + NUMBER = 1, + PAGES = {7--11}, + MONTH = {March}, + URL = {http://CRAN.R-project.org/doc/Rnews/}, + PDF = {http://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf} +} + +@article{Rubin:1981, + author = {Rubin, Donald B.}, + year = {1981}, + title = {Estimation in parallel randomized experiments}, + journal = {Journal of Educational Statistics}, + volume = {6}, + pages = {377--401} +} + +@phdthesis{Schofield:2007, + author = {Schofield, Matthew R.}, + year = {2007}, + title = {Hierarchical Capture-Recapture Models}, + school = {Department of Statistics, University of Otago, Dunedin} +} + +@article{SmithSpiegelhalterThomas:1995, + author = {Smith, Teresa C. and Spiegelhalter, David J. and Thomas, Andrew}, + year = {1995}, + title = {{B}ayesian approaches to random-effects meta-analysis: a comparative study}, + journal = {Statistics in Medicine}, + volume = {14}, + number = {24}, + pages = {2685--2699} +} + +@article{SwendsenWang:1986, + author = {Swendsen, Robert H. and Wang, Jian-Sheng}, + year = {1986}, + title = {Replica {M}onte {C}arlo simulation of spin glasses}, + journal = {Physical Review Letters}, + volume = {57}, + pages = {2607--2609} +} + +@article{Tibshirani:1996, + author = {Tibshirani, Robert}, + year = {1996}, + title = {Regression shrinkage and selection via the lasso}, + journal = {Journal of the Royal Statistical Society, Series B}, + volume = {58}, + number = {1}, + pages = {267--288} +} + +@techreport{TokudaGoodrichVanMechelen:2010, + Author = {Tokuda, Tomoki and Goodrich, Ben and Van Mechelen, Iven and Gelman, Andrew and Tuerlinckx, Francis}, + Date-Added = {2011-10-11 09:21:36 -0400}, + Date-Modified = {2011-10-11 09:22:14 -0400}, + Title = {Visualizing distributions of covariance matrices}, + Institution = {Columbia University, Department of Statistics}, + Url = {http://www.stat.columbia.edu/~gelman/research/unpublished/Visualization.pdf}, + Year = {2010} +} + +@article{Vandekerckhove-Wabersich:2014, + Title = {The {R}{W}iener package: an {R} package providing distribution functions for the {W}iener diffusion model}, + Author = {Joachim Vandekerckhove and Dominik Wabersich}, + Journal = {The R Journal}, + Year = {2014}, + Volume = {6/1}, + Url = {http://journal.r-project.org/archive/2014-1/vandekerckhove-wabersich.pdf}, +} + +@article{Vrugt:2009, + title = {Equifinality of formal ({DREAM}) and informal ({GLUE}) {B}ayesian approaches in hydrologic modeling?}, + author = {Vrugt, J.A. and Ter Braak, C.J.F. and Gupta, H.V. and Robinson, B.A.}, + journal = {Stochastic environmental research and risk assessment}, + volume = {23}, + number = {7}, + pages = {1011--1026}, + year = {2009}, + publisher = {Springer} +} + +@article{WainwrightJordan:2008, + Author = {Wainwright, M.~J. and Jordan, M.~I.}, + Title = {Graphical models, exponential families, and variational inference}, + Volume = {1}, + Pages = {1--305}, + Year = {2008}, + Journal = {Foundations and Trends in Machine Learning} +} + +@article{WarnThompsonSpiegelhalter:2002, + author = {Warn, David E. and Thompson, S.~G. and Spiegelhalter, David J.}, + year = {2002}, + title = {{B}ayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales}, + journal = {Statistics in Medicine}, + volume = {21}, + pages = {1601--1623} +} + +@article{zhang-gp:2004, + title={Inconsistent estimation and asymptotically equal interpolations in model-based geostatistics}, + author={Zhang, Hao}, + journal={Journal of the American Statistical Association}, + volume={99}, + number={465}, + pages={250--261}, + year={2004}, + publisher={Taylor \& Francis} +} + +@inproceedings{Zhang:2011, + title={Quasi-{N}ewton Methods for {M}arkov Chain {M}onte {C}arlo}, + author={Zhang, Y. and Sutton, C.}, + booktitle={Advances in Neural Information Processing Systems 24 ({NIPS})}, + year={2011} +} + +@article{ZouHastie:2005, + author = {Zou, Hui and Hastie, Trevor}, + year = {2005}, + title = {Regularization and variable selection via the elastic net}, + journal = {Journal of the Royal Statistical Society, Series B}, + volume = {67}, + number = {2}, + pages = {301--320} +} + +@article{Takahasi:1974, + title={Double Exponential Formulas for Numerical Integration}, + author={Hidetosi Takahasi and Masatake Mori}, + journal={Publications of the Research Institute for Mathematical Sciences}, + volume={9}, + number={3}, + pages={721-741}, + year={1974}, + doi={10.2977/prims/1195192451} +} + +@article{Mori:1978, + title={An IMT-Type Double Exponential Formula for Numerical Integration}, + author={Masatake Mori}, + journal={Publications of the Research Institute for Mathematical Sciences}, + volume={14}, + number={3}, + pages={713-729}, + year={1978}, + doi={10.2977/prims/1195188835} +} + +@article{Bailey:2005, + author = "Bailey, David H. and Jeyabalan, Karthik and Li, Xiaoye S.", + fjournal = "Experimental Mathematics", + journal = "Experiment. Math.", + number = "3", + pages = "317--329", + publisher = "A K Peters, Ltd.", + title = "A comparison of three high-precision quadrature schemes", + url = "https://projecteuclid.org:443/euclid.em/1128371757", + volume = "14", + year = "2005" +} + +@article{Tanaka:2009, + author="Tanaka, Ken'ichiro + and Sugihara, Masaaki + and Murota, Kazuo + and Mori, Masatake", + title="Function classes for double exponential integration formulas", + journal="Numerische Mathematik", + year="2009", + month="Feb", + day="01", + volume="111", + number="4", + pages="631--655", + issn="0945-3245", + doi="10.1007/s00211-008-0195-1", + url="https://doi.org/10.1007/s00211-008-0195-1" +} + +@article{Ding:18, + author = {Peng Ding and Joseph K. Blitzstein}, + title = {On the Gaussian Mixture Representation of the Laplace Distribution}, + journal = {The American Statistician}, + volume = {72}, + number = {2}, + pages = {172-174}, + year = {2018}, + publisher = {Taylor & Francis}, + doi = {10.1080/00031305.2017.1291448}, + URL = {https://doi.org/10.1080/00031305.2017.1291448}, + eprint = {https://doi.org/10.1080/00031305.2017.1291448} +} + +@article{Hindmarsh:2005, + title={{SUNDIALS}: Suite of nonlinear and differential/algebraic equation solvers}, + author={Hindmarsh, Alan C and Brown, Peter N and Grant, Keith E and Lee, Steven L and Serban, Radu and Shumaker, Dan E and Woodward, Carol S}, + journal={ACM Transactions on Mathematical Software (TOMS)}, + volume={31}, + number={3}, + pages={363--396}, + year={2005}, + publisher={ACM} +} + +@book{Brenan:1996, +author = {Brenan, K. E. and Campbell, S. L. and Petzold, L. R.}, +title = {{N}umerical {S}olution of {Initial-Value} {P}roblems in {Differential-Algebraic} {E}quations}, +publisher = {Society for Industrial and Applied Mathematics}, +year = {1995}, +series = {SIAM Classics in Applied Mathematics}, +number = {14}, +location = {Philadelphia} +} + + +@book{hairer:1993, + address = {Berlin Heidelberg}, + edition = {2}, + series = {Springer {Series} in {Computational} {Mathematics}, {Springer} {Ser}.{Comp}.{Mathem}. {Hairer},{E}.:{Solving} {Ordinary} {Diff}.}, + title = {Solving {Ordinary} {Differential} {Equations} {I}: {Nonstiff} {Problems}}, + publisher = {Springer-Verlag}, + author = {Hairer, Ernst and {Nørsett}, Syvert P. and Wanner, Gerhard}, + year = {1993}, +} + +@article{cashvariable:1990, + title = {A variable order {Runge}-{Kutta} method for initial value problems with rapidly varying right-hand sides}, + volume = {16}, + issn = {0098-3500}, + doi = {10.1145/79505.79507}, + abstract = {Explicit Runge-Kutta methods (RKMs) are among the most popular classes of formulas for the approximate numerical integration of nonstiff, initial value problems. However, high-order Runge-Kutta methods require more function evaluations per integration step than, for example, Adams methods used in PECE mode, and so, with RKMs, it is expecially important to avoid rejected steps. Steps are often rejected when certain derivatives of the solutions are very large for part of the region of integration. This corresponds, for example, to regions where the solution has a sharp front or, in the limit, some derivative of the solution is discontinuous. In these circumstances the assumption that the local truncation error is changing slowly is invalid, and so any step-choosing algorithm is likely to produce an unacceptable step. In this paper we derive a family of explicit Runge-Kutta formulas. Each formula is very efficient for problems with smooth solution as well as problems having rapidly varying solutions. Each member of this family consists of a fifty-order formula that contains imbedded formulas of all orders 1 through 4. By computing solutions at several different orders, it is possible to detect sharp fronts or discontinuities before all the function evaluations defining the full Runge-Kutta step have been computed. We can then either accept a lower order solution or abort the step, depending on which course of action seems appropriate. The efficiency of the new algorithm is demonstrated on the DETEST test set as well as on some difficult test problems with sharp fronts or discontinuities.}, + number = {3}, + urldate = {2021-02-03}, + journal = {ACM Transactions on Mathematical Software}, + author = {Cash, J. R. and Karp, Alan H.}, + month = sep, + year = {1990}, + pages = {201--222}, +} + + +@article{mazziatest:2012, + title = {A {Test} {Set} for stiff {Initial} {Value} {Problem} {Solvers} in the open source software {R}: {Package} {deTestSet}}, + volume = {236}, + issn = {0377-0427}, + shorttitle = {A {Test} {Set} for stiff {Initial} {Value} {Problem} {Solvers} in the open source software {R}}, + language = {en}, + number = {16}, + journal = {Journal of Computational and Applied Mathematics}, + author = {Mazzia, F. and Cash, J. R. and Soetaert, K.}, + month = oct, + year = {2012}, + pages = {4119--4131}, +} + +@book{montenbrucksatellite:2000, + address = {Berlin Heidelberg}, + title = {Satellite {Orbits}: {Models}, {Methods} and {Applications}}, + isbn = {978-3-540-67280-7}, + shorttitle = {Satellite {Orbits}}, + language = {en}, + publisher = {Springer-Verlag}, + author = {Montenbruck, Oliver and Gill, Eberhard}, + year = {2000}, + doi = {10.1007/978-3-642-58351-3}, +} + +@incollection{robertson_solution:1966, + address = {Lodon and New York}, + title = {The solution of a set of reaction rate equations}, + booktitle = {Numerical analysis, an introduction}, + publisher = {Academic Press}, + author = {Robertson, H. H.}, + year = {1966}, + pages = {178--182}, +} +@techreport{serban_example:2021, + title = {Example {Programs} for {IDAS}}, + number = {LLNL-TR-437091}, + institution = {Lawrence Livermore National Laboratory}, + author = {Serban, Radu and Hindmarsh, Alan C.}, + month = dec, + year = {2021}, + pages = {15}, +} + +@misc{serban_user:2021, + title = {User {Documentation} for {IDAS} v5.0.0}, + publisher = {Lawrence Livermore National Laboratory}, + author = {Serban, Radu and Petra, Cosmin and Hindmarsh, Alan C. and Balos, Cody J. and Gardner, David J. and Reynolds, Daniel R. and Woodward, Carol S.}, + month = dec, + year = {2021}, +} + +@book{ascher_computer:1998, + address = {Philadelphia}, + title = {Computer {Methods} for {Ordinary} {Differential} {Equations} and {Differential}-{Algebraic} {Equations}}, + isbn = {978-0-89871-412-8}, + language = {English}, + publisher = {SIAM: Society for Industrial and Applied Mathematics}, + author = {Ascher, Uri M. and Petzold, Linda R.}, + month = jul, + year = {1998}, + file = {Ascher and Petzold - 1998 - Computer Methods for Ordinary Differential Equatio.pdf:/Users/yiz/Zotero/storage/BZ7B6XK8/Ascher and Petzold - 1998 - Computer Methods for Ordinary Differential Equatio.pdf:application/pdf}, +} + +@article{Margossian:2022, + Author = {Margossian, Charles C and Betancourt, Michael}, + Title = {Efficient Automatic Differentiation of Implicit Functions}, + Journal = {Preprint. arXiv:2112.14217.}, + Month = {March}, + Year = {2022}} + +@misc{Gaebler:2021, + Author = {Gaebler, Johann D.}, + Title = {Autodiff for Implicit Functions in Stan}, + Url = {https://www.jgaeb.com/2021/09/13/implicit-autodiff.html#fn:7}, + Month = {September}, + year = {2021} + } + +@article{PullinEtAl:2021, + title = {Statistical Models of Repeated Categorical Ratings: The R package rater}, + author = {Pullin, Jeffrey and Gurrin, Lyle and Vukcevic, Damjan}, + year = {2021}, + journal = {arXiv}, + volume = {2010.09335}, + url = {https://arxiv.org/abs/2010.09335}, +} diff --git a/zissou/community.qmd b/zissou/community.qmd new file mode 100644 index 00000000..e53995ec --- /dev/null +++ b/zissou/community.qmd @@ -0,0 +1,10 @@ +--- +title: "User Support" +--- + +* [Stan forum](https://discourse.mc-stan.org): message board +for questions, discussion, and announcements related to Stan for +both users and developers. + +* Stan slack - developer discussions + diff --git a/zissou/docs.qmd b/zissou/docs.qmd new file mode 100644 index 00000000..218a991d --- /dev/null +++ b/zissou/docs.qmd @@ -0,0 +1,14 @@ +--- +title: "Stan Documentation" +author: "Stan Development Team" +--- + + +* The Stan User's Guide provides example models and programming techniques for coding statistical models in Stan. + +* The Stan Reference Manual specifies the Stan programming language and inference algorithms. + +* The Stan Functions Reference specifies the functions built into the Stan programming language. + +* The CmdStan User's Guide 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zUWIraRGDK5%qD_c}xacIq>sQzrLZC1z&2{=o!wDuDJmgEwl{zAmUs^wQ3Ew_`_ z>!?SU{!x=JdK(u^PY*>I2#gCWlMcIygPfJo-RqATH~Ou^M=rT<53XM-EiJj|)_{xk z#j^GNcDQh0dNg*~9*7I5NW;}CDw(KyPz7t;hQaf^^19<8BR_iX?$fzgZ|UfkBvHZ} z$c2bj}D0%R4d#NahTZX<%&njrc<4?kBmd}JnGj*pw6IeYrEA`382H4KL~RE(ZjU&b*$}s zo=`{#q(-m9zA+Ebo18Vjf#Zm!Ma$gezuWqU-z7j#`X#ZZ4#v_gp!Hp@^2UL1=eE6Q zJvOkC#;dclUF1=q-VnXA3=IJB-5$5w?t(9buOU=2tIDbIsWN*5u(2mT&xOtMsc2%z z7zG~0W3vb_sCuA<^k`|ri-GdEe7lTdRTYhqw)Tr(=t_g?)T+|hNUIs+3x$o}QqX!B*xs6w zo|;lSZI2){3%gzoQcViv-2Hs{G=#PknE*|eoH4m zeu@K%96d3zxREiNC~<1jvaE+v#v>eB3}N2cAbu4tN69T2n1#rA>hucI^i&D4?CE32 zqN`G`xRYrgO+^=Y1V+uqvw+mdw0ew#CHmo@Y zSi!u%llv79xWCn%;OqSN?zMm$r(|DxH80f)14T2fE;gXx=uf12>XiS_G|~T`DZ&Z+ si81T{xBe&0{NG4(Z%^ +Welcome to Stan, the open-source software project for statistical modeling. +

+ +:::{.column-body-outset} +::: {.grid} +::: {.g-col-4} +## [Downloads](install) + +Get the software +::: +::: {.g-col-4} +## [Documentation](docs) + +Read the docs +::: +::: {.g-col-4} +## [Community](community) + +Ask questions,
+get answers,
+discuss solutions +::: +::: +::: + +

+Latest Stan news goes here. +

+ +::: {.grid} +::: {.g-col-3} +::: +::: {.g-col-6} +Stan logo +::: +::: {.g-col-3} +::: +::: + diff --git a/zissou/install.qmd b/zissou/install.qmd new file mode 100644 index 00000000..520e2483 --- /dev/null +++ b/zissou/install.qmd @@ -0,0 +1,12 @@ +--- +title: "Get Stan" +--- + +This page should provide a gallery view of the Stan interfaces and Stan-adjacent tools + +Julia / Python / R + + + + + diff --git a/zissou/styles.css b/zissou/styles.css new file mode 100644 index 00000000..326e330c --- /dev/null +++ b/zissou/styles.css @@ -0,0 +1,80 @@ +/* css styles */ +:root{ + --quarto-body-bg: #FEF8E8; + --quarto-body-color: #052744; + --quarto-text-muted: #053A42; + --bs-body-bg: #FEF8E8; + --bs-body-color: #052744; + --bs-dark: #052744; + --bs-dark-warm: #053A42; + --warm-beige: #F2E5BD; + --palest-beige: #FEF8E8; + --aquatic-blue: #3E8EBC; +} + +a { color: #3E8EBC;} +a.nav-link.active { color: #3E8EBC;} +a.sidebar-item-text.sidebar-link.active { color: #3E8EBC; } +.sidebar.a.active { color: #3E8EBC; } + + +div [data-bs-target^="#quarto-sidebar-section"] { + font-weight: bold; + font-style: italic; + color: #3E8EBC; +} + +p.big-top { + margin-top: 2em; +} + + +pre { + word-break: normal; + word-wrap: normal; +} +pre.sourceCode { + margin: 0; + padding: 0; + color: #053A42; + background-color: #FEFBEE; +} + +pre code { + margin: 0; + padding: 0; + color: #053A42; + background-color: #FEFBEE; +} + +p code:not(.sourceCode) { + color: #053A42; + background-color: #FEFBEE; +} + +code { + color: #053A42; + background-color: #FEFBEE; +} + +#quarto-appendix { + color: #053A42; + background-color: #FEFBEE; +} + +.align-equations { + text-align: left; + margin-left: 2em; /* Adjust as needed */ +} + + +/* headings */ + +h1 { + margin: 1.1em 0em 0.5em 0em; + font-weight: 400; +} +h2 { + margin: 1em 0em 0.4em 0em; + border-bottom: 0px;} +} diff --git a/zissou/support.qmd b/zissou/support.qmd new file mode 100644 index 00000000..1c3b6f09 --- /dev/null +++ b/zissou/support.qmd @@ -0,0 +1,21 @@ +## Contribute Expertise + +We welcome new Stan contributors! + +* Code + +* Write + +* Communicate + + +## Contribute Money + +Stan is now linked to [NumFOCUS](https://numfocus.org/), a +U.S. 501(c)(3) nonprofit organization that serves many open-source software projects +including NumPy, Julia, Jupyter, ScikitLearn. + + +* [Contribute to Stan via NumFOCUS](https://numfocus.salsalabs.org/donate-to-stan)    (Salsa Labs payment processing) + +* [Sponsor Stan via GitHub](https://github.com/sponsors/stan-dev) From 534553258f6ba90ef29d43245009edf9ffc64d20 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Thu, 9 Nov 2023 18:04:01 -0500 Subject: [PATCH 2/8] feedback per Stan meeting --- zissou/_quarto.yml | 23 ++-- zissou/about.qmd | 23 ++-- zissou/index.qmd | 6 +- zissou/styles.css | 319 +++++++++++++++++++++++++++++++++++++++++++-- 4 files changed, 336 insertions(+), 35 deletions(-) diff --git a/zissou/_quarto.yml b/zissou/_quarto.yml index e37308e0..a0fb446f 100644 --- a/zissou/_quarto.yml +++ b/zissou/_quarto.yml @@ -16,7 +16,7 @@ website: foreground: "#F2E5BD" background: "#052744" left: - - text: "Installation" + - text: "Getting Started" href: install.qmd - text: "Documentation" menu: @@ -44,17 +44,19 @@ website: url: https://mc-stan.org/rstanarm - text: "Stan.jl" url: http://stanjulia.github.io/Stan.jl/stable/INTRO/ + - text: "About" + href: about.qmd - text: "Community" href: community.qmd - text: "Contribute" href: support.qmd - - text: "About" - href: about.qmd tools: - icon: github url: https://github.com/stan-dev - icon: mastodon href: "https://bayes.club/%40mcmc_stan" + - icon: youtube + href: "https://www.youtube.com/channel/UCwgN5srGpBH4M-Zc2cAluOA" sidebar: - title: "Stan Users Guide" @@ -141,9 +143,14 @@ website: format: html: - theme: litera - css: styles.css toc: true - fontfamily: sans - - + theme: + light: [cosmo, theme.scss] + code-copy: true + code-overflow: wrap + css: styles.css + include-after-body: js.html + grid: + sidebar-width: 250px + body-width: 900px + margin-width: 300px diff --git a/zissou/about.qmd b/zissou/about.qmd index 00b7c8b7..bd302f79 100644 --- a/zissou/about.qmd +++ b/zissou/about.qmd @@ -1,6 +1,3 @@ -Stan has grown from a 3-person research project started at Columbia University in 2011 to -a global community of developers, researchers, and users. - ## Open Code & Reproducible Science Stan is freedom-respecting, open-source software - - - ## Acknowledgements {-} +Stan has grown from a small research project started in 2011 at Columbia University +to a global community of developers, researchers, and users. The Stan project owes its success to the contributions from hundreds of developers, researchers, active users, and funders. Individual contributions to the software and documentation can be tracked through GitHub. -We are grateful to all the users who have taken the time to file bug reports and -feature requests via the GitHub and the Stan forums; this feedback has greatly -improved Stan's usability and reliability. Stan has been funded through grants for Stan and its developers, through in-kind donations in the form of companies contributing @@ -28,10 +20,13 @@ developer time to Stan and individuals contributing their own time to Stan, and through donations to the open-source scientific software non-profit NumFOCUS. +We are grateful to all the users who have taken the time to file bug reports and +feature requests via the GitHub and the Stan forums; this feedback has greatly +improved Stan's usability and reliability. ## Copyright and trademark {-} -* Copyright 2011--2022, Stan Development Team and their assignees. +* Copyright 2011--2023, Stan Development Team and their assignees. * The Stan name and logo are registered trademarks of NumFOCUS. @@ -43,3 +38,9 @@ non-profit NumFOCUS. * *Computer code:* [BSD 3-clause license](https://opensource.org/licenses/BSD-3-Clause) * *Logo:* [Stan logo usage guidelines](https://mc-stan.org/about/logo/) + +## Citing Stan {-} + +We appreciate citations for the Stan software because it lets us find out what +people have been doing with Stan and motivate further grant funding. See +[How to Cite Stan](https://mc-stan.org/users/citations/) for more details. diff --git a/zissou/index.qmd b/zissou/index.qmd index 575a65ca..04bad9be 100644 --- a/zissou/index.qmd +++ b/zissou/index.qmd @@ -9,17 +9,17 @@ Welcome to Stan, the open-source software project for statistical modeling. :::{.column-body-outset} ::: {.grid} ::: {.g-col-4} -## [Downloads](install) +## [Downloads](install.qmd) Get the software ::: ::: {.g-col-4} -## [Documentation](docs) +## [Documentation](docs/index.qmd) Read the docs ::: ::: {.g-col-4} -## [Community](community) +## [Community](community.qmd) Ask questions,
get answers,
diff --git a/zissou/styles.css b/zissou/styles.css index 326e330c..9b005ab8 100644 --- a/zissou/styles.css +++ b/zissou/styles.css @@ -12,6 +12,312 @@ --aquatic-blue: #3E8EBC; } +/* quarto website styling */ + +@media (min-width: 1020px) { +.navbar-brand-container { + margin-right: 1em; +} +} + +@media (max-width: 1060px) and (min-width: 991.98px) { + +#navbarCollapse ul:last-of-type a.nav-link { + padding-left: .25em; + padding-right: .25em; +} + +.navbar #quarto-search { + margin-left: .1em; +} +} + + +@media (min-width: 991.98px) { +#quarto-header { + border-bottom: 1px solid +} +} + +.navbar-brand > img { + max-height: 36px; +} + +.platform-table td { + vertical-align: middle; +} + +.platform-table td > div.sourceCode { + margin-top: 0.3rem; + margin-bottom: 0.3rem; +} + +.document-example { + opacity: 0.9; + padding: 6px; + font-weight: 500; + margin-bottom: 1rem; +} + +.document-example div { + padding: 5px; +} + +.document-example .citation { + color: blue; +} + +.trademark { + font-size: 0.6rem; + display: inline-block; + margin-left: -3px; +} + +.search-attribution { + margin-top: 20px; + padding-bottom: 20px; + height: 40px; +} + +.download-button { + margin-top: 1em; +} + +.download-table { + margin-bottom: 2em; +} + +.download-table p { + margin-bottom: 0; +} + +.download-table .checksum { + color: var(--bs-primary); + font-size: .775em; + cursor: pointer; + padding-top: 4px; +} + +.download-button { + display:flex; + padding-bottom: 10px; + padding-top: 10px; +} + +.download-button .secondary { + font-size: .775em; + margin-bottom: 0; +} + +.download-button .container { + display: flex; + padding-left: 10px; + padding-right: 40px; +} + +.download-button .icon-container { + fill: white; + width: 30px; + margin-right: 15px; +} + +iframe.reveal-demo { + width: 100%; + height: 350px; + outline: none; +} + +.slide-deck { + border: 3px solid #dee2e6; + width: 100%; + height: 475px; +} + +@media only screen and (max-width: 600px) { + .slide-deck { + height: 400px; + } +} + +@media (max-width: 575px) { +.link-cards .card { + margin-bottom: 20px; + margin-right: 35px; +} + +} + +@media (min-width: 576px) { +.link-cards { + display: flex; + flex-direction: row; + flex-wrap: wrap; +} + +.link-cards .card { + width: 190px; + margin: 0 20px 12px 0; +} + + +} + +.link-cards .card { + border: none; + padding: 0; +} + +.link-cards .card-title h4 { + margin-top: 0; +} + +.link-cards .card-title p { + margin-bottom: 0; +} + +.link-cards .card-subtitle { + margin-bottom: 0.7rem; +} + +.link-cards .card-body { + padding: 0.5rem; + padding-left: 0.1rem; +} + +.link-cards .card-body ul { + margin-bottom: 0; + padding-left: 0; + list-style-type: none; +} + +.link-cards .card-body ul a { + text-decoration: none; +} + +.link-cards .card-body ul li { + padding-bottom: 0.2rem; +} + +.card .source-code { + margin-top: 3px; +} + +.carousel.card { + font-size: 16px; + padding-top: 2em; +} + +.carousel.card a { + text-decoration: none; +} + +.carousel img { + width: 70%; + margin-bottom: 110px; +} + +.carousel .carousel-control-prev-icon, +.carousel .carousel-control-next-icon { + margin-bottom: 110px; +} + +.gallery-category { + column-gap: 10px; +} + +.btn-action-primary { + color: white; + background-color: #447099 !important; +} + +.btn-action-primary:hover { + color: white; +} + +.btn-action { + min-width: 165px; + border-radius: 30px; + border: none; +} + +.panel-tabset[data-group="tools-tabset"] .choose-your-tool { + max-width: 90px; + margin-right: 25px; + margin-top: 30px; + font-weight: 300; + font-size: 1.3rem; + text-align: left; + vertical-align: center; +} + +.panel-tabset[data-group="tools-tabset"] .tab-content { + border: none; + padding-left: 5px; +} + +.panel-tabset[data-group="tools-tabset"] .nav-tabs { + border-bottom: none; +} + +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link { + text-align: center; + margin-right: 10px; + margin-top: 10px; + color: inherit; + width: 102px; + font-size: 0.8em; +} + +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-item.show .nav-link { + border: 1px solid rgb(222, 226, 230); + border-radius: 10px; +} +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link:hover { + border-color: rgb(80,146,221); + border-width: 1px; +} + +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-item.show .nav-link { + border-color: rgb(80,146,221); + border-width: 2px; +} + +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link img { + width: 65px; + height: 65px; + display: block; + margin-bottom: 2px; +} + +.download-text { + font-size: 1.1em; + font-weight: 500; +} + +.preview-image-grid { + gap: .75em; +} + +.preview-image-grid p { + margin-bottom: 0; +} + +.preview-image-label { + text-align: center; + font-size: .75em; + font-weight: 600; +} + +.illustration { + border: 1px solid #dee2e6; +} + +/* stan-dev custom styling */ + + a { color: #3E8EBC;} a.nav-link.active { color: #3E8EBC;} a.sidebar-item-text.sidebar-link.active { color: #3E8EBC; } @@ -28,7 +334,6 @@ p.big-top { margin-top: 2em; } - pre { word-break: normal; word-wrap: normal; @@ -66,15 +371,3 @@ code { text-align: left; margin-left: 2em; /* Adjust as needed */ } - - -/* headings */ - -h1 { - margin: 1.1em 0em 0.5em 0em; - font-weight: 400; -} -h2 { - margin: 1em 0em 0.4em 0em; - border-bottom: 0px;} -} From bf1abe1095ea6d0e28a233c080319c7299ec65fc Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Thu, 9 Nov 2023 18:04:38 -0500 Subject: [PATCH 3/8] feedback per Stan meeting --- zissou/js.html | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 zissou/js.html diff --git a/zissou/js.html b/zissou/js.html new file mode 100644 index 00000000..58075c0d --- /dev/null +++ b/zissou/js.html @@ -0,0 +1,19 @@ + \ No newline at end of file From b9feed2bdced448144e8714b3bcc5f47be138091 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Sun, 12 Nov 2023 13:10:29 -0500 Subject: [PATCH 4/8] tweaks per discussion --- zissou/_quarto.yml | 6 ++--- zissou/about.qmd | 35 +++++++++++++++++++++++++++++ zissou/docs.qmd | 2 +- zissou/img/download_button.svg | 25 +++++++++++++++++++++ zissou/index.qmd | 41 +++++++++++++++++++--------------- zissou/support.qmd | 24 +++++++------------- 6 files changed, 94 insertions(+), 39 deletions(-) create mode 100644 zissou/img/download_button.svg diff --git a/zissou/_quarto.yml b/zissou/_quarto.yml index a0fb446f..1ac97e58 100644 --- a/zissou/_quarto.yml +++ b/zissou/_quarto.yml @@ -44,12 +44,10 @@ website: url: https://mc-stan.org/rstanarm - text: "Stan.jl" url: http://stanjulia.github.io/Stan.jl/stable/INTRO/ + - text: "Discussion" + href: community.qmd - text: "About" href: about.qmd - - text: "Community" - href: community.qmd - - text: "Contribute" - href: support.qmd tools: - icon: github url: https://github.com/stan-dev diff --git a/zissou/about.qmd b/zissou/about.qmd index bd302f79..ca730870 100644 --- a/zissou/about.qmd +++ b/zissou/about.qmd @@ -6,6 +6,7 @@ is associated with NumFOCUS, a 501(c)(3) nonprofit supporting open code and reproducible science, through which you can [help support Stan](support.html#contribute-money). + ## Acknowledgements {-} Stan has grown from a small research project started in 2011 at Columbia University @@ -44,3 +45,37 @@ improved Stan's usability and reliability. We appreciate citations for the Stan software because it lets us find out what people have been doing with Stan and motivate further grant funding. See [How to Cite Stan](https://mc-stan.org/users/citations/) for more details. + +## Contribute Expertise + +We welcome new Stan contributors! + +* Code + +* Write + +* Communicate + + +## Contribute Money + +Stan is now linked to [NumFOCUS](https://numfocus.org/), a +U.S. 501(c)(3) nonprofit organization that serves many open-source software projects +including NumPy, Julia, Jupyter, ScikitLearn. + + +* [Contribute to Stan via NumFOCUS](https://numfocus.salsalabs.org/donate-to-stan)    (Salsa Labs payment processing) + +* [Sponsor Stan via GitHub](https://github.com/sponsors/stan-dev) + +## Stan Shop + +If you're looking to infer in style, stop by one of our shops for Stan +t-shirts and mugs. + +* [Stan Shop [US]](https://shop.spreadshirt.com/mc-stan/) +   (Spreadshirt US) + +* [Stan Shop [UK]](https://shop.spreadshirt.co.uk/mc-stan/) +   (Spreadshirt UK) + diff --git a/zissou/docs.qmd b/zissou/docs.qmd index 218a991d..685727bf 100644 --- a/zissou/docs.qmd +++ b/zissou/docs.qmd @@ -1,6 +1,6 @@ --- title: "Stan Documentation" -author: "Stan Development Team" +toc: false --- diff --git a/zissou/img/download_button.svg b/zissou/img/download_button.svg new file mode 100644 index 00000000..90070415 --- /dev/null +++ b/zissou/img/download_button.svg @@ -0,0 +1,25 @@ + + + + + + Downloads + diff --git a/zissou/index.qmd b/zissou/index.qmd index 04bad9be..f2f755e6 100644 --- a/zissou/index.qmd +++ b/zissou/index.qmd @@ -1,24 +1,34 @@ --- +pagetitle: "Stan" +page-layout: custom toc: false +description: | + Stan is an open-source software project for statistical modeling and Bayesian inference. +hide-description: true --- -

-Welcome to Stan, the open-source software project for statistical modeling. -

+::: {.grid} +::: {.g-col-12} +Stan +::: +::: + -:::{.column-body-outset} ::: {.grid} -::: {.g-col-4} -## [Downloads](install.qmd) +::: {.g-col-1} +::: +::: {.g-col-3} +## [Downloads](docs/install.qmd) Get the software + ::: -::: {.g-col-4} +::: {.g-col-3} ## [Documentation](docs/index.qmd) Read the docs ::: -::: {.g-col-4} +::: {.g-col-3} ## [Community](community.qmd) Ask questions,
@@ -26,19 +36,14 @@ get answers,
discuss solutions ::: ::: -::: -

-Latest Stan news goes here. -

::: {.grid} -::: {.g-col-3} -::: -::: {.g-col-6} -Stan logo +::: {.g-col-1} ::: -::: {.g-col-3} +::: {.g-col-10} +

+Latest Stan news goes here. +

::: ::: - diff --git a/zissou/support.qmd b/zissou/support.qmd index 1c3b6f09..e5e54925 100644 --- a/zissou/support.qmd +++ b/zissou/support.qmd @@ -1,19 +1,11 @@ -## Contribute Expertise - -We welcome new Stan contributors! - -* Code - -* Write - -* Communicate - - -## Contribute Money - -Stan is now linked to [NumFOCUS](https://numfocus.org/), a -U.S. 501(c)(3) nonprofit organization that serves many open-source software projects -including NumPy, Julia, Jupyter, ScikitLearn. +--- +title: "Support Stan" +toc: false +--- + +Stan operates through [NumFOCUS](https://numfocus.org/), a +U.S. 501(c)(3) nonprofit organization that serves open-source software projects +including NumPy, Julia, Jupyter, ScikitLearn, and many more. * [Contribute to Stan via NumFOCUS](https://numfocus.salsalabs.org/donate-to-stan)    (Salsa Labs payment processing) From 0ecc16d27dde8596b5210c19775a4c7b6d1a6842 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Fri, 8 Dec 2023 15:37:49 -0500 Subject: [PATCH 5/8] checkpointing --- zissou/_quarto.yml | 4 +-- zissou/community.qmd | 4 ++- zissou/discussions.qmd | 12 +++++++ zissou/download.qmd | 13 ++++++++ zissou/img/discussion_button.svg | 50 ++++++++++++++++++++++++++++ zissou/img/documentation_button.svg | 51 +++++++++++++++++++++++++++++ zissou/img/download_button.svg | 31 +++++++++++------- zissou/index.qmd | 41 +++++++---------------- 8 files changed, 162 insertions(+), 44 deletions(-) create mode 100644 zissou/discussions.qmd create mode 100644 zissou/download.qmd create mode 100644 zissou/img/discussion_button.svg create mode 100644 zissou/img/documentation_button.svg diff --git a/zissou/_quarto.yml b/zissou/_quarto.yml index 1ac97e58..f1090f79 100644 --- a/zissou/_quarto.yml +++ b/zissou/_quarto.yml @@ -17,7 +17,7 @@ website: background: "#052744" left: - text: "Getting Started" - href: install.qmd + href: download.qmd - text: "Documentation" menu: - text: "Users Guide" @@ -45,7 +45,7 @@ website: - text: "Stan.jl" url: http://stanjulia.github.io/Stan.jl/stable/INTRO/ - text: "Discussion" - href: community.qmd + href: discussions.qmd - text: "About" href: about.qmd tools: diff --git a/zissou/community.qmd b/zissou/community.qmd index e53995ec..20b6de2b 100644 --- a/zissou/community.qmd +++ b/zissou/community.qmd @@ -1,5 +1,5 @@ --- -title: "User Support" +title: "Discussion Forums" --- * [Stan forum](https://discourse.mc-stan.org): message board @@ -8,3 +8,5 @@ both users and developers. * Stan slack - developer discussions +* GitHub issues - report bugs + diff --git a/zissou/discussions.qmd b/zissou/discussions.qmd new file mode 100644 index 00000000..20b6de2b --- /dev/null +++ b/zissou/discussions.qmd @@ -0,0 +1,12 @@ +--- +title: "Discussion Forums" +--- + +* [Stan forum](https://discourse.mc-stan.org): message board +for questions, discussion, and announcements related to Stan for +both users and developers. + +* Stan slack - developer discussions + +* GitHub issues - report bugs + diff --git a/zissou/download.qmd b/zissou/download.qmd new file mode 100644 index 00000000..76424254 --- /dev/null +++ b/zissou/download.qmd @@ -0,0 +1,13 @@ +--- +title: "Download and Install Stan" +--- + +On this page is a grid that allows the user to choose their platform (pre-specified) and programming environment of choice. + +Considerations: C++ toolchain, package managers + + + + + + diff --git a/zissou/img/discussion_button.svg b/zissou/img/discussion_button.svg new file mode 100644 index 00000000..eeba3d79 --- /dev/null +++ b/zissou/img/discussion_button.svg @@ -0,0 +1,50 @@ + + + + + + + + + Discussions + diff --git a/zissou/img/documentation_button.svg b/zissou/img/documentation_button.svg new file mode 100644 index 00000000..acced8da --- /dev/null +++ b/zissou/img/documentation_button.svg @@ -0,0 +1,51 @@ + + + + + + + + + + + Documentation + diff --git a/zissou/img/download_button.svg b/zissou/img/download_button.svg index 90070415..c60ab986 100644 --- a/zissou/img/download_button.svg +++ b/zissou/img/download_button.svg @@ -1,25 +1,32 @@ - + + transform="matrix(6.1715896,0,0,6.8423744,31.539089,24.347778)"> + style="fill:#3e8ebc;fill-opacity:1" + id="path1" /> + style="fill:#3e8ebc;fill-opacity:1;stroke-width:0.3" + id="path2" /> Downloads + x="34.098755" + y="35.73819" + style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:30px;line-height:0%;font-family:'\'Liberation', Arial, 'sans-serif\'';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;text-align:start;letter-spacing:0;word-spacing:0;writing-mode:lr-tb;text-anchor:start;fill:#052744;fill-opacity:1;stroke:none;stroke-width:1" + id="tspan2">Downloads diff --git a/zissou/index.qmd b/zissou/index.qmd index f2f755e6..216a83cb 100644 --- a/zissou/index.qmd +++ b/zissou/index.qmd @@ -1,49 +1,32 @@ --- pagetitle: "Stan" -page-layout: custom toc: false -description: | - Stan is an open-source software project for statistical modeling and Bayesian inference. -hide-description: true --- -::: {.grid} -::: {.g-col-12} -Stan -::: -::: - +

+Welcome to Stan, the open-source software project for statistical modeling. +

::: {.grid} ::: {.g-col-1} ::: ::: {.g-col-3} -## [Downloads](docs/install.qmd) - -Get the software - + +![](img/download_button.svg){width="210" fig-alt="Downloads"} + ::: ::: {.g-col-3} -## [Documentation](docs/index.qmd) - -Read the docs + +![](img/documentation_button.svg){width="220" fig-alt="Downloads"} + ::: ::: {.g-col-3} -## [Community](community.qmd) - -Ask questions,
-get answers,
-discuss solutions + +![](img/discussion_button.svg){width="210" fig-alt="Discussion"} + ::: ::: - -::: {.grid} -::: {.g-col-1} -::: -::: {.g-col-10}

Latest Stan news goes here.

-::: -::: From 3dea142057e8bc787dc8b9c0e2057f73ebc8e99c Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Tue, 20 Feb 2024 09:46:25 -0500 Subject: [PATCH 6/8] checkpointing --- z2/404.qmd | 21 + z2/_quarto.yml | 119 +++ z2/img/logo_tm.png | Bin 0 -> 21065 bytes z2/img/ulam-fermiac.png | Bin 0 -> 370448 bytes z2/index.qmd | 20 + z2/js.html | 19 + z2/learn-stan/case-studies.qmd | 1236 ++++++++++++++++++++++++++++++++ z2/learn-stan/index.qmd | 8 + z2/quarto_styles.css | 416 +++++++++++ z2/quickstart.qmd | 18 + z2/stan.xml | 150 ++++ 11 files changed, 2007 insertions(+) create mode 100644 z2/404.qmd create mode 100644 z2/_quarto.yml create mode 100644 z2/img/logo_tm.png create mode 100644 z2/img/ulam-fermiac.png create mode 100644 z2/index.qmd create mode 100644 z2/js.html create mode 100644 z2/learn-stan/case-studies.qmd create mode 100644 z2/learn-stan/index.qmd create mode 100644 z2/quarto_styles.css create mode 100644 z2/quickstart.qmd create mode 100644 z2/stan.xml diff --git a/z2/404.qmd b/z2/404.qmd new file mode 100644 index 00000000..cc8aae1c --- /dev/null +++ b/z2/404.qmd @@ -0,0 +1,21 @@ +--- +title: Page Not Found +--- + +Ulam + + +The page you requested cannot be found (perhaps it was moved or renamed). + +You may want to try searching to find the page's new location. + +
+ +```stan + + reject("This page cannot be found"); + +``` + +
+ diff --git a/z2/_quarto.yml b/z2/_quarto.yml new file mode 100644 index 00000000..821468c0 --- /dev/null +++ b/z2/_quarto.yml @@ -0,0 +1,119 @@ +project: + title: "Stan" + type: website + output-dir: _website + render: + - index.qmd + - 404.qmd + - mc-stan-org/*.qmd + - learn-stan/*.qmd + resources: + - learn-stan/case-studies/*.html + + +website: + favicon: img/logo_tm.png + image: img/logo_tm.png + page-navigation: true + back-to-top-navigation: true + + navbar: + logo: "img/logo_tm.png" + logo-alt: "Stan logo" + foreground: "#F2E5BD" + background: "#052744" + title: false + left: + - href: index.qmd + - text: "Getting Started" + href: installation/install.qmd + - text: "Documentation" + menu: + - text: "Stan Users Guide" + url: http://localhost:7785/stan-users-guide/overview.html + - text: "Reference Manual" + url: http://localhost:7785/reference-manual/overview.html + - text: "Functions Reference" + url: http://localhost:7785/functions-reference/overview.html + - text: "Examples and Tutorials" + href: learn-stan/index.qmd + - text: "Interfaces" + menu: + - text: "CmdStan" + url: http://localhost:7785/cmdstan-guide/overview.html + - text: "CmdStanPy" + url: https://mc-stan.org/cmdstanpy + - text: "CmdStanR" + url: https://mc-stan.org/cmdstanr + - text: "PyStan" + url: https://mc-stan.org/pystan + - text: "RStan" + url: https://mc-stan.org/rstan + - text: "Stan.jl" + url: http://stanjulia.github.io/Stan.jl/stable/INTRO/ + - text: "Related Tools" + menu: + - text: "Bayesplot" + href: https://mc-stan.org/bayesplot/ + - text: "BridgeStan" + href: https://roualdes.github.io/bridgestan/latest/ + - text: "BRMS" + url: https://paul-buerkner.github.io/brms/ + - text: "loo" + href: https://mc-stan.org/loo/ + - text: "RStanArm" + url: https://mc-stan.org/rstanarm + - text: "Stan Math Library" + url: https://mc-stan.org/math + - text: "About Stan" + href: mc-stan-org/about.qmd + tools: + - icon: github + url: https://github.com/stan-dev + - icon: chat-text-fill + href: "https://discourse.mc-stan.org" + + sidebar: + - id: installation + title: "Download, Install Stan" + foreground: "#052744" + background: "#FEFCF9" + contents: + - installation/install.qmd + + - id: mc-stan-org + title: "Stan Project" + foreground: "#052744" + background: "#FEFCF9" + contents: + - mc-stan-org/about.qmd + - mc-stan-org/license.qmd + - mc-stan-org/community.qmd + - mc-stan-org/support.qmd + - mc-stan-org/shop.qmd + + - id: learn-stan + title: "Examples and Tutorials" + foreground: "#052744" + background: "#FEFCF9" + contents: + - learn-stan/index.qmd + - learn-stan/case-studies.qmd + + +format: + html: + theme: + light: [cosmo, theme.scss] + syntax-definitions: + - stan.xml + highlight-style: tango + code-copy: true + code-overflow: wrap + css: quarto_styles.css + include-after-body: js.html + toc: true + grid: + sidebar-width: 250px + body-width: 900px + margin-width: 300px diff --git a/z2/img/logo_tm.png b/z2/img/logo_tm.png new file mode 100644 index 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- - - - - - - - -

This notebook demonstrates how to implement user defined probability -functions in Stan language. As an example I use the generalized Pareto -distribution (GPD) to model geomagnetic storm data from the World Data -Center for Geomagnetism.

-
-

Setting up the environment

-

Load some libraries:

-
library(extraDistr)
-library(ggplot2)
-library(tidyr)
-library(dplyr)
-library(gridExtra)
-library(rstan)
-library(bayesplot)
-theme_set(bayesplot::theme_default(base_family = "sans"))
-library(loo)
-rstan_options(auto_write = TRUE)
-options(mc.cores = 1)
-
-
-

Data

-

Read the data. This file has magnitudes of 373 geomagnetic storms -which lasted longer than 48h with absolute magnitude larger than 100 in -1957-2014.

-
# file preview shows a header row
-d <- read.csv("geomagnetic_tail_data.csv", header = FALSE)
-# dst are the absolute magnitudes
-colnames(d) <- "dst"
-d <- d %>% mutate(dst = abs(dst)) %>% arrange(dst)
-

Compute the empirical complementary cumulative distribution -function

-
n <- dim(d)[1]
-d$ccdf <- seq(n,1,-1)/n
-head(d)
-
  dst      ccdf
-1 100 1.0000000
-2 100 0.9973190
-3 100 0.9946381
-4 100 0.9919571
-5 100 0.9892761
-6 100 0.9865952
-

Plot just empirical ccdf against the measured magnitudes

-
ggplot() + 
-  geom_point(aes(dst, ccdf), data = d, size = 1, colour = "blue") +
-  coord_trans(x="log10", y="log10", limx=c(100,1000), limy=c(1e-4,1)) +
-  scale_y_continuous(breaks=c(1e-5,1e-4,1e-3,1e-2,1e-1,1), limits=c(1e-4,1)) +
-  scale_x_continuous(breaks=c(100,200,300,400,500,600,700,850,1000), limits=c(100,1000)) +
-  labs(y = "Probability earth's field weakens at least this much", x= "Absolute dst") +
-  geom_text(aes(x = d$dst[n], y = d$ccdf[n]), 
-            label = "Quebec blackout", vjust="top", nudge_y=-0.0005) +
-  guides(linetype = F) + 
-  theme_bw()
-

-

The largest event in the data is the March 1989 geomagnetic storm, -also known as Quebec blackout event (https://en.wikipedia.org/wiki/March_1989_geomagnetic_storm). -Now we are interested in estimating the probability of observing a same -magnitude event in the future which would be helpful, for example, for -insurance companies (for a short term geomagnetic storm predictions -there are very elaborate models using observations closer to sun, too). -Extreme value theory says that many distributions have a tail which is -well modelled with generalized Pareto distribution given the cutoff -point is far enough in the tail. For a more detailed model we could also -take into account that geomagnetic storms are temporally correlated and -tend to appear in bursts.

-
-
-

Generalized Pareto distribution

-

The Generalized Pareto distribution is defined as \[ - p(y|u,\sigma,k)= - \begin{cases} - \frac{1}{\sigma}\left(1+k\left(\frac{y-u}{\sigma}\right)\right)^{-\frac{1}{k}-1}, -& k\neq 0 \\ - \frac{1}{\sigma}\exp\left(\frac{y-u}{\sigma}\right), & k = 0, - \end{cases} -\] where \(u\) is a lower bound -parameter, \(\sigma\) is a scale -parameter, \(k\) is a shape parameter, -and \(y\) is the data restricted to the -range \((u, \inf)\) (see, e.g., https://en.wikipedia.org/wiki/Generalized_Pareto_distribution -for cdf and random number generation).

-

For probability distributions implemented in the Stan math library -there are functions

-
    -
  • _lpdf (or _lpmf): log probability density (mass) function
    • -
  • _cdf: cumulative distribution function
    • -
  • _lcdf: log cumulative distribution function
    • -
  • _lccdf: log complementary cumulative distribution function
    • -
  • _rng: generate random variables from the distribution
    • -
-

Unlike the functions implemented in the C++ Stan math library, the -user defined functions can have only signature. In this example, I have -chosen the function signatures so that the behavior is as close as -possible to most usual ways to call these functions. The main feature is -that all these function return a scalar real value (except _rng would -return scalar integer for a discrete integer valued distribution).

-

For the Generalized Pareto distribution we implement

-
    -
  • real gpareto_lpdf(vector y | real -ymin, real k, real sigma)
    • -
  • real gpareto_cdf(vector y | real -ymin, real k, real sigma)
    • -
  • real gpareto_lcdf(vector y | real -ymin, real k, real sigma)
    • -
  • real gpareto_lccdf(vector y | real -ymin, real k, real sigma)
    • -
  • real gpareto_rng(real ymin, real -k, real sigma)
    • -
-

As we can define only one function signature for user defined -functions, I have chosen to have vector type for y and real -types for the parameters, while builtin functions can handle vectors, -arrays and scalars (denoted by generic type reals). For -vector valued y, _lpdf, _lcdf, and _lccdf return sum of log -values computed with each element of y. For vector valued -y, _cdf returns product of values computed with each element of -y. _rng returns a single random number generated from the -generalized Pareto distribution.

-
-
-

Stan code with user defined functions

-

The whole code for functions, the basic model and the generated -quantities for posterior predictive checking (ppc), leave-one-out -cross-validation (loo), and prediction of rare events is shown below. I -will next go through some practical details.

-
writeLines(readLines("gpareto.stan"))
-
functions {
-  real gpareto_lpdf(vector y, real ymin, real k, real sigma) {
-    // generalised Pareto log pdf
-    int N = rows(y);
-    real inv_k = inv(k);
-    if (k < 0 && max(y - ymin) / sigma > -inv_k) {
-      reject("k<0 and max(y-ymin)/sigma > -1/k; found k, sigma =", k, sigma);
-    }
-    if (sigma <= 0) {
-      reject("sigma<=0; found sigma =", sigma);
-    }
-    if (abs(k) > 1e-15) {
-      return -(1 + inv_k) * sum(log1p((y - ymin) * (k / sigma)))
-             - N * log(sigma);
-    } else {
-      return -sum(y - ymin) / sigma - N * log(sigma);
-    } // limit k->0
-  }
-  real gpareto_cdf(vector y, real ymin, real k, real sigma) {
-    // generalised Pareto cdf
-    real inv_k = inv(k);
-    if (k < 0 && max(y - ymin) / sigma > -inv_k) {
-      reject("k<0 and max(y-ymin)/sigma > -1/k; found k, sigma =", k, sigma);
-    }
-    if (sigma <= 0) {
-      reject("sigma<=0; found sigma =", sigma);
-    }
-    if (abs(k) > 1e-15) {
-      return exp(sum(log1m_exp((-inv_k) * log1p((y - ymin) * (k / sigma)))));
-    } else {
-      return exp(sum(log1m_exp(-(y - ymin) / sigma)));
-    } // limit k->0
-  }
-  real gpareto_lcdf(vector y, real ymin, real k, real sigma) {
-    // generalised Pareto log cdf
-    real inv_k = inv(k);
-    if (k < 0 && max(y - ymin) / sigma > -inv_k) {
-      reject("k<0 and max(y-ymin)/sigma > -1/k; found k, sigma =", k, sigma);
-    }
-    if (sigma <= 0) {
-      reject("sigma<=0; found sigma =", sigma);
-    }
-    if (abs(k) > 1e-15) {
-      return sum(log1m_exp((-inv_k) * log1p((y - ymin) * (k / sigma))));
-    } else {
-      return sum(log1m_exp(-(y - ymin) / sigma));
-    } // limit k->0
-  }
-  real gpareto_lccdf(vector y, real ymin, real k, real sigma) {
-    // generalised Pareto log ccdf
-    real inv_k = inv(k);
-    if (k < 0 && max(y - ymin) / sigma > -inv_k) {
-      reject("k<0 and max(y-ymin)/sigma > -1/k; found k, sigma =", k, sigma);
-    }
-    if (sigma <= 0) {
-      reject("sigma<=0; found sigma =", sigma);
-    }
-    if (abs(k) > 1e-15) {
-      return (-inv_k) * sum(log1p((y - ymin) * (k / sigma)));
-    } else {
-      return -sum(y - ymin) / sigma;
-    } // limit k->0
-  }
-  real gpareto_rng(real ymin, real k, real sigma) {
-    // generalised Pareto rng
-    if (sigma <= 0) {
-      reject("sigma<=0; found sigma =", sigma);
-    }
-    if (abs(k) > 1e-15) {
-      return ymin + (uniform_rng(0, 1) ^ -k - 1) * sigma / k;
-    } else {
-      return ymin - sigma * log(uniform_rng(0, 1));
-    } // limit k->0
-  }
-}
-data {
-  real ymin;
-  int<lower=0> N;
-  vector<lower=ymin>[N] y;
-  int<lower=0> Nt;
-  vector<lower=ymin>[Nt] yt;
-}
-transformed data {
-  real ymax = max(y);
-}
-parameters {
-  real<lower=0> sigma;
-  real<lower=-sigma / (ymax - ymin)> k;
-}
-model {
-  y ~ gpareto(ymin, k, sigma);
-}
-generated quantities {
-  vector[N] log_lik;
-  vector[N] yrep;
-  vector[Nt] predccdf;
-  for (n in 1 : N) {
-    log_lik[n] = gpareto_lpdf(rep_vector(y[n], 1) | ymin, k, sigma);
-    yrep[n] = gpareto_rng(ymin, k, sigma);
-  }
-  for (nt in 1 : Nt) {
-    predccdf[nt] = exp(gpareto_lccdf(rep_vector(yt[nt], 1) | ymin, k, sigma));
-  }
-}
-

For each function we do basic argument checking. In addition of -invalid values due to user errors, due to the limited accuracy of the -floating point presentation of the values, sometimes we may get invalid -parameters. For example, due to the underflow, we may get sigma equal to -0, even if we have declared it as -real<lower=0> sigma;. For these latter cases, it is -useful to use reject statement so the corresponding MCMC -proposal will be rejected and the sampling can continue.

-
    if (k<0 && max(y-ymin)/sigma > -inv_k)
-      reject("k<0 and max(y-ymin)/sigma > -1/k; found k, sigma =", k, sigma)
-    if (sigma<=0)
-      reject("sigma<=0; found sigma =", sigma)
-

Stan documentation warns about use of fabs and -conditional evaluation as they may lead to discontinuous energy or -gradient. In GPD we need to handle a special case of \(k \rightarrow 0\). The following will cause -discontinuity in theory, but the magnitude of the discontinuity is -smaller than the accuracy of the floating point presentation, and thus -this should not cause any additional numerical instability.

-
    if (fabs(k) > 1e-15)
-      return -(1+inv_k)*sum(log1p((y-ymin) * (k/sigma))) -N*log(sigma);
-    else
-      return -sum(y-ymin)/sigma -N*log(sigma); // limit k->0
-

In data block we need to define the minimum threshold ymin. -We also define test points yt.

-
data {
-  real ymin;
-  int<lower=0> N;
-  vector<lower=ymin>[N] y;
-  int<lower=0> Nt;
-  vector<lower=ymin>[Nt] yt;
-}
-

sigma has to be positive and k has a lower limit -which depends on sigma and the maximum value.

-
transformed data {
-  real ymax;
-  ymax = max(y);
-}
-parameters {
-  real<lower=0> sigma; 
-  real<lower=-sigma/(ymax-ymin)> k; 
-}
-

By defining gpareto_lpdf we can use also the common -~ notation in Stan to write familiar looking model -code.

-
model {
-  y ~ gpareto(ymin, k, sigma);
-}
-

In generated quantities we compute log_lik values for -LOO and yrep values for posterior predictive checking. We -had defined the first argument of pareto_lpdf to be a -vector. Now a single element of vector y[n] has a type -real, and as Stan has strong typing and we can’t overload -the user defined functions, we need to cast y[n] to be a -vector by making a 1 element vector rep_vector(y[n],1). -Alternatively we could write another function with a different name -which could accept scalar y[n], but here I wanted to -demonstrate the minimal approach.

-
  for (n in 1:N) {
-    log_lik[n] = gpareto_lpdf(rep_vector(y[n],1) | ymin, k, sigma);
-    yrep[n] = gpareto_rng(ymin, k, sigma);
-  }
-

Finally we compute predictive probabilities for observing events with -certain magnitudes.

-
  for (nt in 1:Nt)
-    predccdf[nt] = exp(gpareto_lccdf(rep_vector(yt[nt],1) | ymin, k, sigma));
-

Before running the Stan model, it’s a good idea to check that we have -not made mistakes in the implementation of the user defined functions. -RStan makes it easy to call the user defined functions in R, and thus -easy to make some checks.

-

Expose the user defined functions to R

-
expose_stan_functions("gpareto.stan")
-# check that integral of exp(gpareto_lpdf) (from ymin to ymax) matches with gpareto_cdf
-gpareto_pdf <- function(y, ymin, k, sigma) {
-  exp(sapply(y, FUN = gpareto_lpdf, ymin = ymin, k = k, sigma = sigma))
-}
-
-
-

Testing the user defined functions

-

Run some tests for the user defined functions

-
# generate random parameter values for the test
-# (repeating the tests with different parameter values is a good thing to do)
-ymin <- rexp(1)
-k <- rexp(1,5)
-sigma <- rexp(1)
-# the first set of tests are testing the self consistency
-# check that exp(gpareto_lpdf)) integrates to 1
-integrate(gpareto_pdf, lower = ymin, upper = Inf,  ymin = ymin, k = k, sigma = sigma)
-
1 with absolute error < 2.6e-08
-
# check that integral of exp(gpareto_lpdf)) from ymin to yr matches gpareto_cdf
-yr <- gpareto_rng(ymin, k, sigma)
-all.equal(integrate(gpareto_pdf, lower = ymin, upper = yr,  ymin = ymin, k = k, sigma = sigma)$value,
-          gpareto_cdf(yr, ymin, k, sigma))
-
[1] TRUE
-
# check that exp(gpareto_lcdf) and gpareto_cdf return the same value
-all.equal(exp(gpareto_lcdf(yr, ymin, k, sigma)),gpareto_cdf(yr, ymin, k, sigma))
-
[1] TRUE
-
# check that exp(gpareto_lcdf) and 1-exp(gpareto_lccdf) return the same value
-all.equal(exp(gpareto_lcdf(yr, ymin, k, sigma)),1-exp(gpareto_lccdf(yr, ymin, k, sigma)))
-
[1] TRUE
-
# the next set of tests compares the duer defined functions to alternative implemenations
-# check that gparetp_lpdf matches dgpd() in extraDistr
-all.equal(gpareto_lpdf(yr, ymin, k, sigma),dgpd(yr, mu = ymin, sigma = sigma, xi = k, log = TRUE))
-
[1] TRUE
-
# check that gparetp_cdf matches pgpd() in extraDistr
-all.equal(gpareto_cdf(yr, ymin, k, sigma),pgpd(yr, mu = ymin, sigma = sigma, xi = k, log = FALSE))
-
[1] TRUE
-
# test that values generated by gpareto_rng have approximately correct sigma and k
-yrs <- replicate(1e6, gpareto_rng(ymin, k, sigma))
-# estimate sigma and k using method by Zhang & Stephens (2009)
-# (you could also use Stan to estimate the parameters)
-th <- loo::gpdfit(yrs-ymin)
-sprintf('True sigma=%.2f, k=%.2f, estimated sigmahat=%.2f, khat=%.2f', sigma, k, th$sigma, th$k)
-
[1] "True sigma=0.24, k=0.19, estimated sigmahat=0.24, khat=0.19"
-
# test separately the special case k=0
-k <- 0
-# the first set of tests are testing the self consistency
-# check that exp(gpareto_lpdf)) integrates to 1
-integrate(gpareto_pdf, lower = ymin, upper = Inf,  ymin = ymin, k = k, sigma = sigma)
-
1 with absolute error < 1.8e-06
-
# check that integral of exp(gpareto_lpdf)) from ymin to yr matches gpareto_cdf
-yr <- gpareto_rng(ymin, k, sigma)
-all.equal(integrate(gpareto_pdf, lower = ymin, upper = yr,  ymin = ymin, k = k, sigma = sigma)$value,
-          gpareto_cdf(yr, ymin, k, sigma))
-
[1] TRUE
-
# check that exp(gpareto_lcdf) and gpareto_cdf return the same value
-all.equal(exp(gpareto_lcdf(yr, ymin, k, sigma)),gpareto_cdf(yr, ymin, k, sigma))
-
[1] TRUE
-
# check that exp(gpareto_lcdf) and 1-exp(gpareto_lccdf) return the same value
-all.equal(exp(gpareto_lcdf(yr, ymin, k, sigma)),1-exp(gpareto_lccdf(yr, ymin, k, sigma)))
-
[1] TRUE
-
# the next set of tests compares the duer defined functions to alternative implemenations
-# check that gparetp_lpdf with k=0 matches dexp() in stats (dgpd in extraDistr does not work with k=0 !!!)
-all.equal(gpareto_lpdf(yr, ymin, k, sigma),dexp(yr-ymin, rate = 1/sigma, log = TRUE))
-
[1] TRUE
-
# check that gparetp_cdf with k=0 matches dexp() in stats (dgpd in extraDistr does not work with k=0 !!!)
-all.equal(gpareto_cdf(yr, ymin, k, sigma),pexp(yr-ymin, rate = 1/sigma, log = FALSE))
-
[1] TRUE
-
# test that values generated by gpareto_rng have approximately correct sigma and k
-yrs <- replicate(1e6, gpareto_rng(ymin, k, sigma))
-# estimate sigma and k using method by Zhang & Stephens (2009)
-# (you could also use Stan to estimate the parameters)
-th <- loo::gpdfit(yrs-ymin)
-sprintf('True sigma=%.2f, k=%.2f, estimated sigmahat=%.2f, khat=%.2f', sigma, k, th$sigma, th$k)
-
[1] "True sigma=0.24, k=0.00, estimated sigmahat=0.24, khat=0.00"
-
-
-

Run Stan

-

Next we use the defined Stan model to analyse the distribution of the -largest geomagnetic storms.

-

Fit the Stan model

-
yt<-append(10^seq(2,3,.01),850)
-ds<-list(ymin=100, N=n, y=d$dst, Nt=length(yt), yt=yt)
-fit_gpd <- stan(file='gpareto.stan', data=ds, refresh=0,
-                     chains=4, seed=100)
-
-
-

MCMC diagnostics

-

Run the usual diagnostics (see Robust -Statistical Workflow with RStan)

-
monitor(as.array(fit_gpd, pars=c("k","sigma")))
-
Inference for the input samples (4 chains: each with iter = 1000; warmup = 500):
-
-        Q5  Q50  Q95 Mean  SD  Rhat Bulk_ESS Tail_ESS
-k      0.1  0.2  0.4  0.3 0.1  1.00      724      747
-sigma 37.8 43.8 50.3 44.0 3.8  1.01      763     1066
-
-For each parameter, Bulk_ESS and Tail_ESS are crude measures of 
-effective sample size for bulk and tail quantities respectively (an ESS > 100 
-per chain is considered good), and Rhat is the potential scale reduction 
-factor on rank normalized split chains (at convergence, Rhat <= 1.05).
-
check_hmc_diagnostics(fit_gpd)
-

-Divergences:
-
-Tree depth:
-
-Energy:
-

The diagnostics do not reveal anything alarming.

-
-
-

Predictions

-

Plot the model fit with 90% posterior interval. The largest observed -magnitude in the data corresponds to Quebec blackout in 1989 (https://en.wikipedia.org/wiki/March_1989_geomagnetic_storm). -The vertical dashed line at 850 shows the estimated magnitude of the -Carrington event in 1859 (https://en.wikipedia.org/wiki/Solar_storm_of_1859).

-
gpd_params <- rstan::extract(fit_gpd)
-mu <- apply(t(gpd_params$predccdf), 1, quantile, c(0.05, 0.5, 0.95)) %>%
-  t() %>% data.frame(x = yt, .) %>% gather(pct, y, -x)
-clrs <- color_scheme_get("brightblue")
-ggplot() + 
-  geom_point(aes(dst, ccdf), data = d, size = 1, color = clrs[[5]]) +
-  geom_line(aes(x=c(850,850),y=c(1e-4,1)),linetype="dashed",color="red") +
-  geom_line(aes(x, y, linetype = pct), data = mu, color = 'red') +
-  scale_linetype_manual(values = c(2,1,2)) +
-  coord_trans(x="log10", y="log10", limx=c(100,1000), limy=c(1e-4,1)) +
-  scale_y_continuous(breaks=c(1e-5,1e-4,1e-3,1e-2,1e-1,1), limits=c(1e-4,1)) +
-  scale_x_continuous(breaks=c(100,200,300,400,500,600,700,850,1000), limits=c(100,1000)) +
-  geom_text(aes(x = d$dst[n], y = d$ccdf[n]), label = "Quebec blackout", vjust="top", nudge_y=-0.0005) +
-  geom_text(aes(x = 820, y = 0.02), label = "Carrington event", angle=90) +
-  labs(y = "Probability earth's field weakens at least this much", x= "Absolute dst") +
-  guides(linetype = F) + 
-  theme_bw()
-

-
-
-

Posterior and LOO predictive checking

-

For additional model checking plot 1) kernel density estimate of -log(dst) and posterior predictive replicates, 2) max log magnitude -(Quebec event) and histogram of maximums of posterior predictive -replicates, 3) leave-one-out cross-validation -probability-integral-transformation, and 4) khat values from the -PSIS-LOO. None of these diagnostics reveal problems in the model -fit.

-
ppc1 <- ppc_dens_overlay(log(d$dst),log(gpd_params$yrep[1:50,])) + labs(x="log(absolute dst)")
-ppc2 <- ppc_stat(log(d$dst), log(gpd_params$yrep), stat = "max") + labs(x="max(log(absolute dst))")
-psiss <- psis(-gpd_params$log_lik)
-clrs <- color_scheme_get("brightblue")
-pkhats <- ggplot() + geom_point(aes(x=seq(1,n),y=psiss$diagnostics$pareto_k), color=clrs[[5]]) + labs(y="khat", x="") +
-  geom_hline(yintercept=0, linetype="dashed") + ylim(-0.25,1) + theme_default()
-ppc3 <- ppc_loo_pit_qq(log(d$dst), log(gpd_params$yrep), lw=psiss$log_weights)
-grid.arrange(ppc1,ppc2,ppc3,pkhats,ncol=2)
-

-

We compute also PSIS-LOO estimate, although this is not much use -without an alternative model to compare against.

-
(loo_gpd<-loo(fit_gpd))
-

-Computed from 4000 by 373 log-likelihood matrix
-
-         Estimate   SE
-elpd_loo  -1874.7 23.7
-p_loo         1.8  0.2
-looic      3749.4 47.3
-------
-Monte Carlo SE of elpd_loo is 0.0.
-
-All Pareto k estimates are good (k < 0.5).
-See help('pareto-k-diagnostic') for details.
-
-
-

Conclusion on the data analysis

-

Here the analysis was simplified by assuming the geomagnetic storm -events to be independent in time although they appear in bursts. The -Generalized Pareto distribution can be used for correlated data, but we -should really take the correlation into account for predicting the -magnitude of storms in the future . Based on the simplified independence -assumption though, and assuming same number of events larger than 100 in -the next 57 years, we can compute

-
round(mean(1-(1-gpd_params$predccdf[,78])^(373)),2)
-
[1] 0.79
-
round(mean(1-(1-gpd_params$predccdf[,102])^(373)),2)
-
[1] 0.4
-

That is, the probability that we would observe Quebec blackout -magnitude event in the next 57 years is 80% and the probability of -observing Carrington level event in the next 57 years is 40%.

-

Now go read about the effects of these geomagnetic storms!

-


-
-

Appendix: Session information

-
sessionInfo()
-
R version 4.2.2 Patched (2022-11-10 r83330)
-Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
-
-Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
-
-locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=fi_FI.UTF-8        LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=fi_FI.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=fi_FI.UTF-8       LC_NAME=C                 
- [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=fi_FI.UTF-8 LC_IDENTIFICATION=C       
-
-attached base packages:
-[1] stats     graphics  grDevices utils     datasets  methods   base     
-
-other attached packages:
-[1] loo_2.6.0           bayesplot_1.10.0    rstan_2.26.23      
-[4] StanHeaders_2.26.28 gridExtra_2.3       dplyr_1.1.3        
-[7] tidyr_1.3.0         ggplot2_3.4.3       extraDistr_1.9.1   
-
-loaded via a namespace (and not attached):
- [1] Rcpp_1.0.11          lattice_0.20-45      QuickJSR_1.0.5      
- [4] prettyunits_1.1.1    ps_1.7.5             digest_0.6.33       
- [7] utf8_1.2.3           R6_2.5.1             plyr_1.8.8          
-[10] backports_1.4.1      stats4_4.2.2         evaluate_0.21       
-[13] highr_0.10           pillar_1.9.0         rlang_1.1.1         
-[16] rstudioapi_0.15.0    callr_3.7.3          jquerylib_0.1.4     
-[19] Matrix_1.5-1         checkmate_2.2.0      rmarkdown_2.24      
-[22] labeling_0.4.3       stringr_1.5.0        RcppEigen_0.3.3.9.3 
-[25] munsell_0.5.0        compiler_4.2.2       xfun_0.40           
-[28] pkgconfig_2.0.3      pkgbuild_1.4.2       rstantools_2.3.1.1  
-[31] htmltools_0.5.6      tidyselect_1.2.0     tensorA_0.36.2      
-[34] tibble_3.2.1         codetools_0.2-19     matrixStats_1.0.0   
-[37] fansi_1.0.4          crayon_1.5.2         withr_2.5.0         
-[40] distributional_0.3.2 BH_1.81.0-1          grid_4.2.2          
-[43] jsonlite_1.8.7       gtable_0.3.4         lifecycle_1.0.3     
-[46] posterior_1.4.1      magrittr_2.0.3       scales_1.2.1        
-[49] RcppParallel_5.1.7   cli_3.6.1            stringi_1.7.12      
-[52] cachem_1.0.8         farver_2.1.1         reshape2_1.4.4      
-[55] bslib_0.5.1          generics_0.1.3       vctrs_0.6.3         
-[58] tools_4.2.2          glue_1.6.2           purrr_1.0.2         
-[61] abind_1.4-5          processx_3.8.2       parallel_4.2.2      
-[64] fastmap_1.1.1        yaml_2.3.7           inline_0.3.19       
-[67] colorspace_2.1-0     knitr_1.43           sass_0.4.7
-


-
-
-

Appendix: Acknowledgments

-

I thank Ben Goodrich, Ben Bales, and Bob Carpenter for useful -comments on the draft of this notebook.

-
-
-

Appendix: Licenses

-
    -
  • Code © 2017, Aki Vehtari, licensed under BSD-3.
    • -
  • Text © 2017, Aki Vehtari, licensed under CC-BY 4.0.
    • -
-
-
- - - - -
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However, high-order Runge-Kutta methods require more function evaluations per integration step than, for example, Adams methods used in PECE mode, and so, with RKMs, it is expecially important to avoid rejected steps. Steps are often rejected when certain derivatives of the solutions are very large for part of the region of integration. This corresponds, for example, to regions where the solution has a sharp front or, in the limit, some derivative of the solution is discontinuous. In these circumstances the assumption that the local truncation error is changing slowly is invalid, and so any step-choosing algorithm is likely to produce an unacceptable step. In this paper we derive a family of explicit Runge-Kutta formulas. Each formula is very efficient for problems with smooth solution as well as problems having rapidly varying solutions. Each member of this family consists of a fifty-order formula that contains imbedded formulas of all orders 1 through 4. By computing solutions at several different orders, it is possible to detect sharp fronts or discontinuities before all the function evaluations defining the full Runge-Kutta step have been computed. We can then either accept a lower order solution or abort the step, depending on which course of action seems appropriate. The efficiency of the new algorithm is demonstrated on the DETEST test set as well as on some difficult test problems with sharp fronts or discontinuities.}, - number = {3}, - urldate = {2021-02-03}, - journal = {ACM Transactions on Mathematical Software}, - author = {Cash, J. R. and Karp, Alan H.}, - month = sep, - year = {1990}, - pages = {201--222}, -} - - -@article{mazziatest:2012, - title = {A {Test} {Set} for stiff {Initial} {Value} {Problem} {Solvers} in the open source software {R}: {Package} {deTestSet}}, - volume = {236}, - issn = {0377-0427}, - shorttitle = {A {Test} {Set} for stiff {Initial} {Value} {Problem} {Solvers} in the open source software {R}}, - language = {en}, - number = {16}, - journal = {Journal of Computational and Applied Mathematics}, - author = {Mazzia, F. and Cash, J. R. and Soetaert, K.}, - month = oct, - year = {2012}, - pages = {4119--4131}, -} - -@book{montenbrucksatellite:2000, - address = {Berlin Heidelberg}, - title = {Satellite {Orbits}: {Models}, {Methods} and {Applications}}, - isbn = {978-3-540-67280-7}, - shorttitle = {Satellite {Orbits}}, - language = {en}, - publisher = {Springer-Verlag}, - author = {Montenbruck, Oliver and Gill, Eberhard}, - year = {2000}, - doi = {10.1007/978-3-642-58351-3}, -} - -@incollection{robertson_solution:1966, - address = {Lodon and New York}, - title = {The solution of a set of reaction rate equations}, - booktitle = {Numerical analysis, an introduction}, - publisher = {Academic Press}, - author = {Robertson, H. H.}, - year = {1966}, - pages = {178--182}, -} -@techreport{serban_example:2021, - title = {Example {Programs} for {IDAS}}, - number = {LLNL-TR-437091}, - institution = {Lawrence Livermore National Laboratory}, - author = {Serban, Radu and Hindmarsh, Alan C.}, - month = dec, - year = {2021}, - pages = {15}, -} - -@misc{serban_user:2021, - title = {User {Documentation} for {IDAS} v5.0.0}, - publisher = {Lawrence Livermore National Laboratory}, - author = {Serban, Radu and Petra, Cosmin and Hindmarsh, Alan C. and Balos, Cody J. and Gardner, David J. and Reynolds, Daniel R. and Woodward, Carol S.}, - month = dec, - year = {2021}, -} - -@book{ascher_computer:1998, - address = {Philadelphia}, - title = {Computer {Methods} for {Ordinary} {Differential} {Equations} and {Differential}-{Algebraic} {Equations}}, - isbn = {978-0-89871-412-8}, - language = {English}, - publisher = {SIAM: Society for Industrial and Applied Mathematics}, - author = {Ascher, Uri M. and Petzold, Linda R.}, - month = jul, - year = {1998}, - file = {Ascher and Petzold - 1998 - Computer Methods for Ordinary Differential Equatio.pdf:/Users/yiz/Zotero/storage/BZ7B6XK8/Ascher and Petzold - 1998 - Computer Methods for Ordinary Differential Equatio.pdf:application/pdf}, -} - -@article{Margossian:2022, - Author = {Margossian, Charles C and Betancourt, Michael}, - Title = {Efficient Automatic Differentiation of Implicit Functions}, - Journal = {Preprint. arXiv:2112.14217.}, - Month = {March}, - Year = {2022}} - -@misc{Gaebler:2021, - Author = {Gaebler, Johann D.}, - Title = {Autodiff for Implicit Functions in Stan}, - Url = {https://www.jgaeb.com/2021/09/13/implicit-autodiff.html#fn:7}, - Month = {September}, - year = {2021} - } - -@article{PullinEtAl:2021, - title = {Statistical Models of Repeated Categorical Ratings: The R package rater}, - author = {Pullin, Jeffrey and Gurrin, Lyle and Vukcevic, Damjan}, - year = {2021}, - journal = {arXiv}, - volume = {2010.09335}, - url = {https://arxiv.org/abs/2010.09335}, -} diff --git a/zissou/community.qmd b/zissou/community.qmd deleted file mode 100644 index 20b6de2b..00000000 --- a/zissou/community.qmd +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: "Discussion Forums" ---- - -* [Stan forum](https://discourse.mc-stan.org): message board -for questions, discussion, and announcements related to Stan for -both users and developers. - -* Stan slack - developer discussions - -* GitHub issues - report bugs - diff --git a/zissou/discussions.qmd b/zissou/discussions.qmd deleted file mode 100644 index 20b6de2b..00000000 --- a/zissou/discussions.qmd +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: "Discussion Forums" ---- - -* [Stan forum](https://discourse.mc-stan.org): message board -for questions, discussion, and announcements related to Stan for -both users and developers. - -* Stan slack - developer discussions - -* GitHub issues - report bugs - diff --git a/zissou/docs.qmd b/zissou/docs.qmd deleted file mode 100644 index 685727bf..00000000 --- a/zissou/docs.qmd +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: "Stan Documentation" -toc: false ---- - - -* The Stan User's Guide provides example models and programming techniques for coding statistical models in Stan. - -* The Stan Reference Manual specifies the Stan programming language and inference algorithms. - -* The Stan Functions Reference specifies the functions built into the Stan programming language. - -* The CmdStan User's Guide documents the command-line interface to the Stan inference engine. - diff --git a/zissou/download.qmd b/zissou/download.qmd deleted file mode 100644 index 76424254..00000000 --- a/zissou/download.qmd +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: "Download and Install Stan" ---- - -On this page is a grid that allows the user to choose their platform (pre-specified) and programming environment of choice. - -Considerations: C++ toolchain, package managers - - - - - - diff --git a/zissou/img/discussion_button.svg 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zo=`{#q(-m9zA+Ebo18Vjf#Zm!Ma$gezuWqU-z7j#`X#ZZ4#v_gp!Hp@^2UL1=eE6Q zJvOkC#;dclUF1=q-VnXA3=IJB-5$5w?t(9buOU=2tIDbIsWN*5u(2mT&xOtMsc2%z z7zG~0W3vb_sCuA<^k`|ri-GdEe7lTdRTYhqw)Tr(=t_g?)T+|hNUIs+3x$o}QqX!B*xs6w zo|;lSZI2){3%gzoQcViv-2Hs{G=#PknE*|eoH4m zeu@K%96d3zxREiNC~<1jvaE+v#v>eB3}N2cAbu4tN69T2n1#rA>hucI^i&D4?CE32 zqN`G`xRYrgO+^=Y1V+uqvw+mdw0ew#CHmo@Y zSi!u%llv79xWCn%;OqSN?zMm$r(|DxH80f)14T2fE;gXx=uf12>XiS_G|~T`DZ&Z+ si81T{xBe&0{NG4(Z%^ -Welcome to Stan, the open-source software project for statistical modeling. -

- -::: {.grid} -::: {.g-col-1} -::: -::: {.g-col-3} - -![](img/download_button.svg){width="210" fig-alt="Downloads"} - -::: -::: {.g-col-3} - -![](img/documentation_button.svg){width="220" fig-alt="Downloads"} - -::: -::: {.g-col-3} - -![](img/discussion_button.svg){width="210" fig-alt="Discussion"} - -::: -::: - -

-Latest Stan news goes here. -

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color: #3E8EBC; -} - -p.big-top { - margin-top: 2em; -} - -pre { - word-break: normal; - word-wrap: normal; -} -pre.sourceCode { - margin: 0; - padding: 0; - color: #053A42; - background-color: #FEFBEE; -} - -pre code { - margin: 0; - padding: 0; - color: #053A42; - background-color: #FEFBEE; -} - -p code:not(.sourceCode) { - color: #053A42; - background-color: #FEFBEE; -} - -code { - color: #053A42; - background-color: #FEFBEE; -} - -#quarto-appendix { - color: #053A42; - background-color: #FEFBEE; -} - -.align-equations { - text-align: left; - margin-left: 2em; /* Adjust as needed */ -} diff --git a/zissou/support.qmd b/zissou/support.qmd deleted file mode 100644 index e5e54925..00000000 --- a/zissou/support.qmd +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: "Support Stan" -toc: false ---- - -Stan operates through [NumFOCUS](https://numfocus.org/), a -U.S. 501(c)(3) nonprofit organization that serves open-source software projects -including NumPy, Julia, Jupyter, ScikitLearn, and many more. - - -* [Contribute to Stan via NumFOCUS](https://numfocus.salsalabs.org/donate-to-stan)    (Salsa Labs payment processing) - -* [Sponsor Stan via GitHub](https://github.com/sponsors/stan-dev) From 9cb259604de176f0021ecb8e89d85cce41195a8d Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Mon, 8 Apr 2024 12:02:27 -0400 Subject: [PATCH 8/8] checkpointing --- z2/_quarto.yml | 72 ++++------ z2/index.qmd | 55 ++++++-- z2/learn-stan/index.qmd | 8 -- z2/misc/built-on-stan.qmd | 26 ++++ z2/quickstart.qmd | 18 --- z2/theming/nord.theme | 182 +++++++++++++++++++++++++ z2/{ => theming}/quarto_styles.css | 112 +++++++++------- z2/{ => theming}/stan.xml | 0 z2/theming/tango.theme | 207 +++++++++++++++++++++++++++++ z2/theming/theme-colors.scss | 11 ++ z2/theming/theme-dark.scss | 58 ++++++++ z2/theming/theme.scss | 28 ++++ 12 files changed, 653 insertions(+), 124 deletions(-) delete mode 100644 z2/learn-stan/index.qmd create mode 100644 z2/misc/built-on-stan.qmd delete mode 100644 z2/quickstart.qmd create mode 100644 z2/theming/nord.theme rename z2/{ => theming}/quarto_styles.css (78%) rename z2/{ => theming}/stan.xml (100%) create mode 100644 z2/theming/tango.theme create mode 100644 z2/theming/theme-colors.scss create mode 100644 z2/theming/theme-dark.scss create mode 100644 z2/theming/theme.scss diff --git a/z2/_quarto.yml b/z2/_quarto.yml index 821468c0..020f6c64 100644 --- a/z2/_quarto.yml +++ b/z2/_quarto.yml @@ -4,13 +4,14 @@ project: output-dir: _website render: - index.qmd + - quickstart.qmd - 404.qmd - - mc-stan-org/*.qmd + - installation/*.qmd - learn-stan/*.qmd + - mc-stan-org/*.qmd resources: - learn-stan/case-studies/*.html - website: favicon: img/logo_tm.png image: img/logo_tm.png @@ -20,27 +21,34 @@ website: navbar: logo: "img/logo_tm.png" logo-alt: "Stan logo" - foreground: "#F2E5BD" - background: "#052744" title: false left: - - href: index.qmd + - text: "Stan" + href: index.qmd - text: "Getting Started" href: installation/install.qmd - text: "Documentation" menu: - text: "Stan Users Guide" - url: http://localhost:7785/stan-users-guide/overview.html + url: https://mc-stan.org/docs/stan-users-guide/index.html - text: "Reference Manual" - url: http://localhost:7785/reference-manual/overview.html + url: https://mc-stan.org/docs/reference-manual/index.html - text: "Functions Reference" - url: http://localhost:7785/functions-reference/overview.html - - text: "Examples and Tutorials" - href: learn-stan/index.qmd + url: https://mc-stan.org/docs/functions-reference/overview.html + - text: "Guides and Tutorials" + menu: + - text: "Case Studies" + href: learn-stan/case-studies.qmd + - text: "Field Guides" + href: learn-stan/field-guides.qmd + - text: "Tutorials" + href: learn-stan/tutorials.qmd + - text: "Journal Articles" + href: learn-stan/articles.qmd - text: "Interfaces" menu: - text: "CmdStan" - url: http://localhost:7785/cmdstan-guide/overview.html + url: https://mc-stan.org/docs/cmdstan-guide/overview.html - text: "CmdStanPy" url: https://mc-stan.org/cmdstanpy - text: "CmdStanR" @@ -65,7 +73,7 @@ website: url: https://mc-stan.org/rstanarm - text: "Stan Math Library" url: https://mc-stan.org/math - - text: "About Stan" + - text: "About" href: mc-stan-org/about.qmd tools: - icon: github @@ -73,45 +81,19 @@ website: - icon: chat-text-fill href: "https://discourse.mc-stan.org" - sidebar: - - id: installation - title: "Download, Install Stan" - foreground: "#052744" - background: "#FEFCF9" - contents: - - installation/install.qmd - - - id: mc-stan-org - title: "Stan Project" - foreground: "#052744" - background: "#FEFCF9" - contents: - - mc-stan-org/about.qmd - - mc-stan-org/license.qmd - - mc-stan-org/community.qmd - - mc-stan-org/support.qmd - - mc-stan-org/shop.qmd - - - id: learn-stan - title: "Examples and Tutorials" - foreground: "#052744" - background: "#FEFCF9" - contents: - - learn-stan/index.qmd - - learn-stan/case-studies.qmd - - format: html: theme: - light: [cosmo, theme.scss] + light: [cosmo, theming/theme.scss] + dark: [cosmo, theming/theme-dark.scss] syntax-definitions: - - stan.xml - highlight-style: tango + - theming/stan.xml + highlight-style: + light: theming/tango.theme + dark: theming/nord.theme code-copy: true code-overflow: wrap - css: quarto_styles.css - include-after-body: js.html + css: theming/quarto_styles.css toc: true grid: sidebar-width: 250px diff --git a/z2/index.qmd b/z2/index.qmd index bf7de054..27a1061a 100644 --- a/z2/index.qmd +++ b/z2/index.qmd @@ -1,20 +1,59 @@ --- -title: "Stan" +pagetitle: "Stan" toc: false +page-layout: custom +section-divs: false +hide-description: true +description: | + Stan combines powerful statistical modeling capabilities with user-friendly interfaces, an active community, and a commitment to open-source development. --- -Stan logo +::: {.hero-banner} -This is the Stan Home Page +::: {.grid} -The most important information is +::: {.g-col-1} +::: -a) How to install Stan +::: {.g-col-11} -b) How to use Stan +# Welcome to the Stan Project! -c) How to get help +## Open-Source Software for Statistical Modeling -d) How to contribute +Stan combines powerful statistical modeling capabilities, an active community, and a commitment to open-source development. +Interfaces for Python, Julia, R, and the Unix shell make it easy to use Stan in any programming environment, +from laptops, clusters, or the cloud. +::: {.hero-buttons} +[Get Started](installation/install.qmd){.btn-action-primary .btn-action .btn .btn-success .btn-lg role="button"} +::: + +::: +::: +::: + + +::: {style="position: relative; display: flex; justify-content: center;"} + +::: {.grid} + +::: {.g-col-11} + +## Join the Community Discussions + +* [Stan forum](https://discourse.mc-stan.org): message board +for questions, discussion, and announcements related to Stan for +both users and developers. + +* Stan slack - developer discussions + +* GitHub issues - report bugs + + +Follow us on Mastodon! + +::: +::: +::: diff --git a/z2/learn-stan/index.qmd b/z2/learn-stan/index.qmd deleted file mode 100644 index ba03f624..00000000 --- a/z2/learn-stan/index.qmd +++ /dev/null @@ -1,8 +0,0 @@ ---- -title: "Examples and Tutorials" ---- - -Members of the Stan community have contributed tutorials, case studies, and field guides. - - - diff --git a/z2/misc/built-on-stan.qmd b/z2/misc/built-on-stan.qmd new file mode 100644 index 00000000..61206c86 --- /dev/null +++ b/z2/misc/built-on-stan.qmd @@ -0,0 +1,26 @@ +## Software Packages which Use Stan + +* McElreath, R.   *rethinking: Statistical Rethinking book + package*, version 1.58. GitHub project rmcelreath/rethinking. + Language: R + +* Bürkner, P.-C.   *brms: Bayesian Regression Models using Stan*. + CRAN package brms. Language: R + +* Facebook.   [*PROPHET: Forecasting at + Scale*](https://facebookincubator.github.io/prophet/). + Languages: R and Python + +Additional CRAN packages using Stan in R can be found in the reverse +links from the packages +[rstan](https://cran.r-project.org/web/packages/rstan/index.html) +and +[rstanarm](https://cran.r-project.org/web/packages/rstanarm/index.html). + +## Software using the No-U-Turn Sampler + +* ICON, plc. 2017. *NONMEM 7.4, Nonlinear mixed effects models for + pharmacometrics*. [commercial paid license, *not* open source] + +* PyMC Developers. 2016. *PyMC3: Probabilistic programming in + Python*. GitHub project pymc-devs/pymc3. diff --git a/z2/quickstart.qmd b/z2/quickstart.qmd deleted file mode 100644 index f14e205f..00000000 --- a/z2/quickstart.qmd +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: "Getting Started" -toc: false ---- - -Stan logo - -### Hello World - - -Run Stan's demo model: example/bernoulli.stan - - -- compile Stan model to executable program -- run program + data -- get output summary -- do downstream analysis - diff --git a/z2/theming/nord.theme b/z2/theming/nord.theme new file mode 100644 index 00000000..193f9846 --- /dev/null +++ b/z2/theming/nord.theme @@ -0,0 +1,182 @@ +{ + "_comments": [ + "Last update: Sep 21, 2020 (revision 2)", + "This theme has been adapted from: https://www.nordtheme.com" + ], + "metadata": { + "copyright": [ + "SPDX-FileCopyrightText: 2016 Arctic Ice Studio ", + "SPDX-FileCopyrightText: 2016 Sven Greb ", + "SPDX-FileCopyrightText: 2020 Nibaldo González " + ], + "license": "SPDX-License-Identifier: MIT", + "name": "Nord", + "revision": 2 + }, + "editor-colors": { + "BackgroundColor": "#2e3440", + "BracketMatching": "#4c566a", + "CodeFolding": "#434c5e", + "CurrentLine": "#3b4252", + "CurrentLineNumber": "#616e88", + "IconBorder": "#2e3440", + "IndentationLine": "#434c5e", + "LineNumbers": "#4c566a", + "MarkBookmark": "#55749a", + "MarkBreakpointActive": "#79aab9", + "MarkBreakpointDisabled": "#b48ead", + "MarkBreakpointReached": "#91a97e", + "MarkError": "#a95862", + "MarkExecution": "#4c566a", + "MarkWarning": "#d0b47d", + "ModifiedLines": "#ebcb8b", + "ReplaceHighlight": "#8e6057", + "SavedLines": "#a3be8c", + "SearchHighlight": "#5f8491", + "Separator": "#3b4252", + "SpellChecking": "#bf616a", + "TabMarker": "#3e4656", + "TemplateBackground": "#3b4252", + "TemplateFocusedPlaceholder": "#81a1c1", + "TemplatePlaceholder": "#5e81ac", + "TemplateReadOnlyPlaceholder": "#4c566a", + "TextSelection": "#4c566a", + "WordWrapMarker": "#3f4859" + }, + "background-color": "#2e3440", + "text-styles": { + "Alert": { + 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b/z2/theming/quarto_styles.css @@ -1,18 +1,3 @@ -/* css styles */ -:root{ - --old-quarto-body-bg: #FEFBF2; - --quarto-body-bg: #FFFDF9; - --quarto-body-color: #052744; - --quarto-text-muted: #053A42; - --bs-body-bg: #FEFBF2; - --bs-body-color: #052744; - --bs-dark: #052744; - --bs-dark-warm: #053A42; - --warm-beige: #F2E5BD; - --palest-beige: #FEFCF9; - --aquatic-blue: #3E8EBC; -} - /* quarto website styling */ @media (min-width: 1020px) { @@ -40,6 +25,7 @@ } } + .navbar-brand > img { max-height: 36px; } @@ -55,7 +41,7 @@ .document-example { opacity: 0.9; - padding: 6px; + padding: 6px; font-weight: 500; margin-bottom: 1rem; } @@ -65,7 +51,7 @@ } .document-example .citation { - color: var(--aquatic-blue); + color: var(--stan-secondary); } .trademark { @@ -85,7 +71,7 @@ } .download-table { - margin-bottom: 2em; + margin-bottom: 2em; } .download-table p { @@ -148,7 +134,7 @@ iframe.reveal-demo { } -@media (min-width: 576px) { +@media (min-width: 576px) { .link-cards { display: flex; flex-direction: row; @@ -217,7 +203,7 @@ iframe.reveal-demo { margin-bottom: 110px; } -.carousel .carousel-control-prev-icon, +.carousel .carousel-control-prev-icon, .carousel .carousel-control-next-icon { margin-bottom: 110px; } @@ -227,8 +213,8 @@ iframe.reveal-demo { } .btn-action-primary { - color: var(--palest-biege); - background-color: var(--aquatic-blue) !important; + color: var(--stan-bg); + background-color: var(--stan-secondary) !important; } .btn-action-primary:hover { @@ -269,20 +255,20 @@ iframe.reveal-demo { font-size: 0.8em; } -.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link, -.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, .panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-item.show .nav-link { - border: 1px solid var(--warm-beige); + border: 1px solid var(--stan-highlight); border-radius: 10px; } .panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link:hover { - border-color: var(--aquatic-blue); + border-color: var(--stan-secondary); border-width: 1px; -} +} -.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, +.panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-link.active, .panel-tabset[data-group="tools-tabset"] .nav-tabs .nav-item.show .nav-link { - border-color: var(--aquatic-blue); + border-color: var(--stan-secondary); border-width: 2px; } @@ -295,7 +281,7 @@ iframe.reveal-demo { .download-text { font-size: 1.1em; - font-weight: 500; + font-weight: 500; } .preview-image-grid { @@ -318,16 +304,16 @@ iframe.reveal-demo { /* stan-dev custom styling */ -a { color: var(--aquatic-blue);} -a.nav-link.active { color: var(--aquatic-blue);} -a.sidebar-item-text.sidebar-link.active { color: var(--aquatic-blue); } -.sidebar.a.active { color: var(--aquatic-blue); } +a { color: var(--stan-secondary);} +a.nav-link.active { color: var(--stan-secondary);} +a.sidebar-item-text.sidebar-link.active { color: var(--stan-secondary); } +.sidebar.a.active { color: var(--stan-secondary); } div [data-bs-target^="#quarto-sidebar-section"] { font-weight: bold; font-style: italic; - color: var(--aquatic-blue); + color: var(--stan-secondary); } p.big-top { @@ -337,42 +323,47 @@ p.big-top { code { white-space: inherit; color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } code:not(.sourceCode) { color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } p code { white-space: inherit; color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } p code:not(.sourceCode) { color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } li code:not(.sourceCode) { color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); +} + +td code:not(.sourceCode) { + color: var(--bs-dark-warm); + background-color: var(--stan-bg); } pre { word-break: normal; word-wrap: normal; color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } pre code { white-space: inherit; margin: 0; padding: 0; color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } pre.sourceCode { @@ -380,12 +371,12 @@ pre.sourceCode { margin: 0; padding: 0; color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } #quarto-appendix { color: var(--bs-dark-warm); - background-color: var(--palest-beige); + background-color: var(--stan-bg); } .align-equations { @@ -410,7 +401,38 @@ pre.sourceCode { font-weight: bold; font-style: italic; font-size: 1.3em; - color: var(--aquatic-blue); + color: var(--stan-secondary); margin-above: -20em; padding: 0; } + + +/* quarto landing page styling */ + + +.hero-banner { + position: relative; + display: flex; + justify-content: center; + color: var(--stan-highlight); + background-color: var(--stan-dark); +} + +.hero-banner h1 { + font-size: 2.5rem; +} + +.hero-banner .hero-image { + position: absolute; + display: none; + height: auto; +} + +.hero-banner .content-block { + display: flex; + flex-direction: row; +} + +.hero-banner .content-block .hero-text { + width: 75%; +} diff --git a/z2/stan.xml b/z2/theming/stan.xml similarity index 100% rename from z2/stan.xml rename to z2/theming/stan.xml diff --git a/z2/theming/tango.theme b/z2/theming/tango.theme new file mode 100644 index 00000000..178110eb --- /dev/null +++ b/z2/theming/tango.theme @@ -0,0 +1,207 @@ +{ + "_comments": [ + "generated with `quarto pandoc --print-highlight-style tango 2> tango.theme`" + ], + "text-color": null, + "background-color": "#f8f8f8", + "line-number-color": "#aaaaaa", + "line-number-background-color": null, + "text-styles": { + "Alert": { + "text-color": "#ef2929", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Annotation": { + "text-color": "#8f5902", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Attribute": { + "text-color": "#204a87", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "BaseN": { + "text-color": "#0000cf", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Char": { + "text-color": "#4e9a06", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Comment": { + "text-color": "#8f5902", + "background-color": null, + "bold": false, + "italic": true, + "underline": false + }, + "CommentVar": { + "text-color": "#8f5902", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Constant": { + "text-color": "#8f5902", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "ControlFlow": { + "text-color": "#204a87", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "DataType": { + "text-color": "#204a87", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "DecVal": { + "text-color": "#0000cf", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Documentation": { + "text-color": "#8f5902", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Error": { + "text-color": "#a40000", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "Extension": { + "text-color": null, + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Float": { + "text-color": "#0000cf", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Function": { + "text-color": "#204a87", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "Import": { + "text-color": null, + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Information": { + "text-color": "#8f5902", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + }, + "Keyword": { + "text-color": "#204a87", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "Operator": { + "text-color": "#ce5c00", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "Other": { + "text-color": "#8f5902", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Preprocessor": { + "text-color": "#8f5902", + "background-color": null, + "bold": false, + "italic": true, + "underline": false + }, + "SpecialChar": { + "text-color": "#ce5c00", + "background-color": null, + "bold": true, + "italic": false, + "underline": false + }, + "SpecialString": { + "text-color": "#4e9a06", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "String": { + "text-color": "#4e9a06", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Variable": { + "text-color": "#000000", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "VerbatimString": { + "text-color": "#4e9a06", + "background-color": null, + "bold": false, + "italic": false, + "underline": false + }, + "Warning": { + "text-color": "#8f5902", + "background-color": null, + "bold": true, + "italic": true, + "underline": false + } + } +} diff --git a/z2/theming/theme-colors.scss b/z2/theming/theme-colors.scss new file mode 100644 index 00000000..a6384bcf --- /dev/null +++ b/z2/theming/theme-colors.scss @@ -0,0 +1,11 @@ +/*-- scss:defaults --*/ + + +$warm-beige: #F2E5BD; +$palest-beige: #FEFCF9; +$aquatic-blue: #3E8EBC; +$navy-blue: #052744; +$warm-gray: #053A42; + +$pale-aquatic-blue: #AED1E4; +$off-white: #FAF1E4; diff --git a/z2/theming/theme-dark.scss b/z2/theming/theme-dark.scss new file mode 100644 index 00000000..f5bafd5b --- /dev/null +++ b/z2/theming/theme-dark.scss @@ -0,0 +1,58 @@ +/*-- scss:defaults --*/ + + +@import "theme-colors"; + + +// Base document colors +$body-bg: #181818; +$body-color: $off-white; +$link-color: $pale-aquatic-blue; + +$light: #525252; + +// Navigation element colors +$footer-bg: #181818; +$navbar-bg: #303030; + + +// Code blocks +$code-block-bg-alpha: -.8; + +// Bootstrap popovers +$popover-bg: #242424; + +// Bootstrap inputs +$input-bg: #242424; +$dropdown-bg: $body-bg; +$dropdown-color: $body-color; +$dropdown-link-color: $body-color; +$dropdown-link-hover-bg: $gray-600; +$dropdown-link-hover-color: $off-white; + +$quarto-body-bg: $body-bg; +$quarto-body-color: $body-color; +$bs-body-bg: $body-bg; +$bs-body-color: $body-color; + + +$bs-dark-warm: $palest-beige; + + +$sidebar-bg: #353535; +$sidebar-fg: $off-white; + +// re-export the colors as CSS variables +:root { + --stan-bg: #242424; + --stan-highlight: #{$warm-gray}; + --stan-secondary: #{$pale-aquatic-blue}; + --stan-dark: #{$palest-beige}; +} + + +/*-- scss:rules --*/ + +.layout-example { + background: $gray-700; + } diff --git a/z2/theming/theme.scss b/z2/theming/theme.scss new file mode 100644 index 00000000..f2b59047 --- /dev/null +++ b/z2/theming/theme.scss @@ -0,0 +1,28 @@ +/*-- scss:defaults --*/ + +@import "theme-colors"; + +$old-quarto-body-bg: #FEFBF2; +$body-bg: #FEFBF2; + +$quarto-body-color: $navy-blue; +$quarto-text-muted: $warm-gray; + +$bs-body-color: $navy-blue; +$bs-dark: $navy-blue; +$bs-dark-warm: $warm-gray; + +$navbar-fg: $warm-beige; +$navbar-bg: $navy-blue; +$sidebar-bg: $palest-beige; +$sidebar-fg: $navy-blue; + + + +// re-export the colors as CSS variables +:root { + --stan-bg: #{$palest-beige}; + --stan-highlight: #{$warm-beige}; + --stan-secondary: #{$aquatic-blue}; + --stan-dark: #{$navy-blue}; +}

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a/z2/js.html b/z2/js.html new file mode 100644 index 00000000..58075c0d --- /dev/null +++ b/z2/js.html @@ -0,0 +1,19 @@ + \ No newline at end of file diff --git a/z2/learn-stan/case-studies.qmd b/z2/learn-stan/case-studies.qmd new file mode 100644 index 00000000..e7b44076 --- /dev/null +++ b/z2/learn-stan/case-studies.qmd @@ -0,0 +1,1236 @@ +--- +pagetitle: Case Studies +page-layout: full +toc-location: left +--- + +# Stan Case Studies + +The case studies on this page are intended to reflect best practices in Bayesian methodology and Stan programming. We aim to keep them current with the latest version of the Stan language, but there may be times when case studies need updating to reflect the latest Stan features and syntax. + +## Volume 10 (2023) + +### Instrumental Variables Analysis of Randomized Experiments with One-Sided Noncompliance + +In this document, we demonstrate how to implement Bayesian inference for causal effects in randomized experiments with one-sided noncompliance using Stan. Specifically, we aim to replicate the analysis presented in Imbens and Rubin (1997). We present Stan models with and without the exclusion restriction assumption, showcasing a significant advantage of the Bayesian model-based approach. + + +View HTML +*(link opens in new tab)* + +Authors +: JoonHo Lee, Avi Feller, Sophia Rabe-Hesketh + +Keywords +: causal inference, instrumental variables analysis, one-sided compliance, principal stratification + +Source Repository +: [example-models/education/causal_iv_one-sided](https://github.com/joonho112/example-models/tree/master/education/causal_iv_one-sided) +(GitHub) + +Dependencies +: `tidyverse`, `rstan`, `bayesplot`, `patchwork` + +License +: BSD (3 clause), CC-BY + +### Bayesian Structural Equation Modeling using blavaan + +In this case study, we fit Bayesian structural equation models (SEM) using Hamiltonian Monte Carlo sampling in Stan-powered R package *blavaan* and illustrate how to use confirmtory factor analysis and latent growth curve modeling as SEM's special cases. We also compared the estimates from *blavaan* with its frequentist counterpart using *lavaan*. + + +View HTML +(*Link opens in new tab*) + +Authors +: Feng Ji, Xingyao Xiao, Aybolek Amanmyradova, Sophia Rabe-Hesketh + +Keywords +: Structural Equation Modeling (SEM), Lavant Variable Modeling, Latent Growth Curve Models, Confirmatory Factor Analysis (CFA), Growth Curve Modeling, +Bayesian Model Evaluation + +Source Repository +: [example-models/education/sem](https://github.com/education-stan/example-models/tree/master/education/sem) +(GitHub) + +Dependencies +: `blavaan, lavaan, rstan, MASS, mvtnorm, tidyverse, semPlot, magrittr, lavaan.survey` + +License +: BSD (3 clause), CC-BY + +## Volume 9 (2022) + +### Multilevel regression modeling with CmdStanPy and plotnine + +This notebook is a short introduction to multilevel regression modeling +using the [CmdStanPy](https://mc-stan.org/cmdstanpy/) interface and +[plotnine](https://plotnine.readthedocs.io/en/stable/), a Python implementation +of a [_grammar of graphics_](https://vita.had.co.nz/papers/layered-grammar.pdf) +based on [ggplot2](https://en.wikipedia.org/wiki/Ggplot2). + + +[View](case-studies/radon_cmdstanpy_plotnine.html) (HTML) + +Authors +: Mitzi Morris + +Keywords +: Python, CmdStanPy, plotnine, hierarchical/multilevel modeling, +linear regression, posterior predictive checks, radon + +Source Repository +: [example-models/jupyter/radon](https://github.com/stan-dev/example-models/tree/master/jupyter/radon) +(GitHub) + +Dependencies +: `cmdstanpy`, `numpy`, `pandas`, `matplotlib`, `plotnine`, `jupyter` + +License +: BSD (3 clause), CC-BY + + + +### HoloML in Stan: Low-photon Image Reconstruction + +In this case study, we perform image reconstruction in Stan by implementing the HoloML phase retrieval model and then solving the inverse problem with optimization. This case study requires Stan 2.30 or greater in order to use the Fourier transform functions added in that version. + +[View](case-studies/HoloML_in_Stan.html) (HTML) + +Authors +: Brian Ward, Bob Carpenter, and David Barmherzig + +Keywords +: image reconstruction, phase retrieval, Fourier transforms, deconvolution + +Source Repository +: [WardBrian/holoml-in-stan](https://github.com/WardBrian/holoml-in-stan) +(GitHub) + +Dependencies +: `cmdstanpy`, `numpy`, `matplotlib`, `scipy`, `jupyter` + +License +: BSD (3 clause), CC-BY + + + +## Volume 8 (2021) + +### Bayesian Latent Class Models and Handling of Label Switching + +In this case study, we fit the Bayesian latent class model using Hamiltonian Monte Carlo sampling and Variational Bayes in Stan and illustrate the issue of label switching and its treatment with simulated and empirical data. + +[View](case-studies/Latent_class_case_study.html) (HTML) + +Authors +: Feng Ji, Aybolek Amanmyradova, Sophia Rabe-Hesketh + +Keywords +: latent class models, label-switching, post-hoc relabeling, variational Bayes + +Source Repository +: [example-models/education/latent_class](https://github.com/education-stan/example-models/tree/master/education/latent_class) +(GitHub) + +Dependencies +: `label.switching`, `rstan`, `magrittr`, `knitr`, `poLCA` + +License +: BSD (3 clause), CC-BY + + +## Volume 7 (2020) + +### Bayesian model of planetary motion: exploring ideas for a modeling workflow when dealing with ordinary differential equations and multimodality + +The Bayesian model of planetary motion is a simple but powerful example that illustrates important concepts, as well as gaps, in prescribed modeling workflows. Our focus is on Bayesian inference using Markov chains Monte Carlo for a model based on an ordinary differential equations (ODE). Our example presents unexpected multimodality, causing our inference to be unreliable and what is more, dramatically slowing down our ODE integrators. What do we do when our chains do not mix and do not forget their starting points? Reasoning about the computational statistics at hand and the physics of the modeled phenomenon, we diagnose how the modes arise and how to improve our inference. Our process for fitting the model is iterative, starting with a simplification and building the model back up, and makes extensive use of visualization. + +[View](case-studies/planetary_motion/planetary_motion.html) (HTML) + +Authors +: Charles Margossian and Andrew Gelman + +Keywords +: ordinary differential equations, multimodality, classical mechanics + +Source Repository +: [example-models/knitr/planetary_motion](https://github.com/stan-dev/example-models/tree/master/knitr/planetary_motion) +(GitHub) + +Dependencies +: `CmdStanR`, `posterior`, `ggplot2`, `dplyr`, `plyr`, `tidyr`, `boot`, `latex2exp` + +License +: BSD (3 clause), CC-BY + +### HMM Interface Example + +Cmdstan 2.24 introduces a new interface for working with +Hidden Markov Models (HMMs). This is an example of how to use that +interface. + +[View](case-studies/hmm-example.html) (HTML) + +Authors +: Ben Bales + +Keywords +: Hidden Markov Models, HMMs, cmdstanr, Stan programming + +Source Repository +: [example-models/knitr/hmm-example](https://github.com/stan-dev/example-models/tree/master/knitr/hmm-example) +(GitHub) + +Dependencies +: `CmdStanR`, `tidyverse`, `ggplot2`, `posterior` + +License +: BSD (3 clause), CC-BY + +### Spatial models for plant neighborhood dynamics in Stan + +In this case study, we demonstrate how Stan’s segment function can speed computation on sparse matrices of pairwise neighbors in plant-plant interaction models. In addition, we present solutions to common problems of fitting neighborhood models with hierarchical effects, including a comparison of centered vs. non-centered parameterizations. + +[View](case-studies/plantInteractions.html) (HTML) + +Authors +: Cristina Barber, Andrii Zaiats, Cara Applestein and T.Trevor Caughlin + +Keywords +: plants, neighbor interactions, sparse matrix, segment function + +Source Repository +: [Cristinabarber/Neighbor_Interactions](https://github.com/Cristinabarber/Neighbor_Interactions) +(GitHub) + +R Package Dependencies +: `rstan` + +License +: BSD (3 clause), CC BY NC + + +### Predicting Engine Failure with Hierarchical Gaussian Process + +This gaussian process case study is an extension of the StanCon [talk](https://www.youtube.com/watch?v=1FJyNntNMH4&feature=youtu.be) +"Failure prediction in hierarchical equipment system: spline fitting naval ship failure". +Many comparison criteria exist, but in terms of prediction accuracy, the gaussian process model outperformed the spline model. However, this accuracy comes at a cost of a more detailed and iterative checking process. This case study shows how identification and underfitting problems diagnosed from pushforward and predictive checks are addressed through reparameterization and adding variables. Basically, our data is highly unbalanced per category with lots of missing data. Also, due to the hierarchical structure of the system, such as shared engine types, the hierarchical model is applicable. For a detailed explanation of the data and spline model, please refer to +[this notebook](https://github.com/hyunjimoon/reliability_prediction/blob/master/spline/Failure%20prediction%20in%20hierarchical%20equipment%20system.ipynb). + + +[View](case-studies/gaussian-process.html) (HTML) + +Author +: Hyunji Moon, Jungin Choi + +Keywords +: Hierarchical Gaussian process, Bayesian workflow + +Source Repository +: [example-models/knitr/gaussian-process](https://github.com/stan-dev/example-models/tree/master/knitr/gaussian-process) +(GitHub) + +Dependencies +: `CmdStanR`, `Rstan` + +License +: BSD (3 clause), CC-BY + +### Upgrading to the new ODE interface + +Cmdstan 2.24 introduces a new ODE interface intended to make it easier to +specify the ODE system function. This document should serve as an overview of the +interface changes as well as a tutorial for converting code written with the old +ODE interface. + +[View](case-studies/convert_odes.html) (HTML) + +Authors +: Ben Bales, Sebastian Weber + +Keywords +: ordinary differential equations, cmdstanr, Stan programming + +Source Repository +: [example-models/knitr/convert-odes](https://github.com/stan-dev/example-models/tree/master/knitr/convert-odes) +(GitHub) + +Dependencies +: `CmdStanR` + +License +: BSD (3 clause), CC-BY + +### Bayesian Workflow for disease transmission modeling in Stan + + +This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the COVID-19 outbreak and doing Bayesian inference. +Bayesian modeling provides a principled way to quantify uncertainty and incorporate prior knowledge into the model. +What is more, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means we can verify whether our inference is reliable. +Stan is an expressive probabilistic programing language that abstracts the inference and allows users to focus on the modeling. The resulting code is readable and easily extensible, which makes the modeler's work more transparent and flexible. +In this tutorial, we demonstrate with a simple Susceptible-Infected-Recovered (SIR) model how to formulate, fit, and diagnose a compartmental model in Stan. +We also introduce more advanced topics which can help practitioners fit sophisticated models; notably, how to use simulations to probe our model and our priors, and computational techniques to scale ODE-based models. + + +[View](case-studies/boarding_school_case_study.html) (HTML) + +Authors +: Leo Grinsztajn, Elizaveta Semenova, Charles C. Margossian, and Julien Riou + +Keywords +: Disease transmission, Compartment models, Ordinary Differential Equations, Bayesian Workflow + +Source Repository +: [charlesm93/disease\_transmission\_workflow](https://github.com/charlesm93/disease_transmission_workflow) +(GitHub) + +Dependencies +: `RStan` + +License +: BSD (3 clause), CC-BY + +### Reduce Sum Example: parallelization of a single chain across multiple cores + +Stan 2.23 introduced `reduce_sum`, a new way to parallelize the execution +of a single Stan chain across multiple cores. This introduction copies directly +from Richard McElreath’s +[Multithreading and Map-Reduce in Stan 2.18.0: A Minimal Example](https://github.com/rmcelreath/cmdstan_map_rect_tutorial) + + + +[View](case-studies/reduce_sum_tutorial.html) (HTML) + +Author +: Ben Bales + +Keywords +: within-chain parallel computation, cmdstanr, Stan programming + +Source Repository +: [example-models/knitr/reduce-sum](https://github.com/stan-dev/example-models/tree/master/knitr/reduce-sum) +(GitHub) + +Dependencies +: `CmdStanR` + +License +: BSD (3 clause), CC-BY + + + +### Stan Notebooks in the Cloud + +This report shows you how to author a Jupyter Notebook for your Stan +model and data so that anyone with a modern web browser and a Google account +can run your analysis with Google Colaboratory free cloud servers. It shows +you how to quickly set up a Stan installation in the cloud and introduces +two lightweight interfaces: +[CmdStanR](https://mc-stan.org/cmdstanr/articles/cmdstanr.html) and +[CmdStanPy](https://cmdstanpy.readthedocs.io/en/latest/index.html). + + +[View](case-studies/jupyter_colab_notebooks_2020.html) (HTML) + +Author +: Mitzi Morris + +Keywords +: Jupyter, Google Colab, teaching Stan, online classroom, cloud computing + +Source Repository +: [example-models/knitr/cloud-compute-2020](https://github.com/stan-dev/example-models/tree/master/knitr/cloud-compute-2020) +(GitHub) + +Dependencies +: internet connection, Google account + +License +: BSD (3 clause), CC-BY + + + +## Volume 6 (2019) + + +### Model-based Inference for Causal Effects in Completely Randomized Experiments + +In this document, we discuss the implementation of Bayesian model-based inference for causal effects in Stan. We start by providing an introduction to the Bayesian inferential framework by analyzing a simulated dataset generated under unconfounded treatment assignment. Then we analyze an example dataset obtained from a completely randomized experiment focusing on the specification of the joint distribution of the potential outcomes. + +[View](case-studies/model-based_causal_inference_for_RCT.html) (HTML) + +Author +: JoonHo Lee, Avi Feller and Sophia Rabe-Hesketh + +Keywords +: causal inference, completely randomized experiments + +Source Repository +: [example-models/education/causal_rct](https://github.com/stan-dev/example-models/tree/master/education/causal_rct) + +R Package Dependencies +: `rstan`, `rstanarm`, `bayesplot`, `tidyverse`, `gridExtra`, `Matching` + +License +: BSD (3 clause), CC-BY + + +### Tagging Basketball Events with HMM in Stan + +This case study shows how we can apply Bayesian inference to Hidden Markov Models (HMMs) using Stan to extract useful information from basketball player tracking data. Specifically we show how to tag drive events and how to determine defensive assignment. Before diving into basketball data we show how to fit an HMM in Stan using a simple example. This should help build some intuition for those who are unfamiliar with HMMs and will also show how to specify an HMM using Stan. + +[View](case-studies/bball-hmm.html) (HTML) + +Author +: Imad Ali + +Keywords +: hidden markov models, sports + +Source Repository +: [imadmali/bball-hmm](https://github.com/imadmali/bball-hmm) +(GitHub) + +R Package Dependencies +: `rstan`, `bayesplot`, `dplyr` + +License +: BSD (3 clause), CC-BY-NC + + +### Model building and expansion for golf putting + +In this case study, we use Stan to build a series of models to estimate the probability of a successful putt using data from professional golfers. We fit and check the fit of a series of models, demonstrating the benefits of modeling based on substantive (rather than purely statistical) principles. We successfully fit to a small dataset and then have to expand the model to fit a new, larger dataset. We use weakly informative priors and a model-misfit error term to enable the fit. + +[View](case-studies/golf.html) (HTML) + +Author +: Andrew Gelman + +Keywords +: nonlinear regression, sports + +Source Repository +: [example-models/knitr/golf](https://github.com/stan-dev/example-models/tree/master/knitr/golf) +(GitHub) + +R Package Dependencies +: `rstan` + +License +: BSD (3 clause), CC-BY + +### A Dyadic Item Response Theory Model: Stan Case Study + +In this case study, we use Stan to fit the Dyadic Item Response Theory (dIRT) model proposed by (Gin et al. 2019) to measure interactions between pairs of individuals when the responses to items represent the actions/behaviors/perceptions of an individual (called the 'actor') made within the context of a dyad formed with another individual (called the 'partner'). The dIRT model is fit using Stan (version 2.18.1) in R via the rstan package. + +[View](case-studies/dyadic_irt_model.html) (HTML) + +Author +: Nicholas Sim, Brian Gin, Anders Skrondal and Sophia Rabe-Hesketh + +Keywords +: item response theory, social relations model, dyadic data + +Source Repository +: [example-models/education/dyadic_irt_model](https://github.com/education-stan/example-models/tree/master/education/dyadic_irt_model) +(GitHub) + +R Package Dependencies +: `rstan`, `tidyverse` + +License +: BSD (3 clause), CC-BY + +## Volume 5 (2018) + +### Multilevel Linear Models using Rstanarm + +In this tutorial, we illustrate how to fit a multilevel linear model within a full Bayesian framework using rstanarm. This tutorial is aimed primarily at educational researchers who have used lme4 in R to fit models to their data and who may be interested in learning how to fit Bayesian multilevel models. However, for readers who have not used lme4 before, we briefly review the use of the package for fitting multilevel models. + +[View](case-studies/tutorial_rstanarm.html) (HTML) + +Author +: JoonHo Lee, Nicholas Sim, Feng Ji, and Sophia Rabe-Hesketh + +Keywords +: education, rstanarm, multilevel models, linear mixed models, hierarchical linear models + +Source Repository +: [example-models/education/tutorial_rstanarm](https://github.com/stan-dev/example-models/tree/master/education/tutorial_rstanarm) +(GitHub) + +R Package Dependencies +: `rstanarm`, `mlmRev`, `ggplot2`, `lme4` + +License +: BSD (3 clause), CC-BY + +### Predator-Prey Population Dynamics: the Lotka-Volterra model in Stan + +Lotka (1925) and Volterra (1926) formulated parametric differential +equations that characterize the oscillating populations of predators +and prey. A statistical model to account for measurement error and +unexplained variation uses the deterministic solutions to the +Lotka-Volterra equations as expected population sizes. Stan is used to +encode the statistical model and perform full Bayesian inference to +solve the inverse problem of inferring parameters from noisy data. The +model is fit to Canadian lynx and snowshoe hare populations between +1900 and 1920, based on the number of pelts collected annually by the +Hudson’s Bay Company. Posterior predictive checks for replicated data +show the model fits this data well. Full Bayesian inference may be +used to estimate future (or past) populations. + +[View](case-studies/lotka-volterra-predator-prey.html) (HTML) + +Author +: Bob Carpenter + +Keywords +: population dynamics, Lotka-Volterra equations, differential +equations, posterior predictive checks + +Source Repository +: [stan-dev/example-models/knitr/lotka-volterra]() +(GitHub) + +R Package Dependencies +: `rstan`, `>ggplot2`, `gridExtra`, `knitr`, `reshape`, `tufte` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + + +### Nearest neighbor Gaussian process (NNGP) models in Stan + + +Nearest neighbor Gaussian process (NNGP) based models is a +family of highly scalable Gaussian processes based models. In brief, +NNGP extends the Vecchia’s approximation (Vecchia 1988) to a process +using conditional independence given information from neighboring +locations. This case study shows how to express and fit these models +in Stan. + + +[View](case-studies/nngp.html) (HTML) + +Author +: Lu Zhang + +Keywords +: Gaussian process, nearest neighbor Gaussian process, spatial models, +latent process, regression + +Source Repository +: [LuZhangstat/NNGP_STAN](https://github.com/LuZhangstat/NNGP_STAN) +(GitHub) + +R Package Dependencies +: `rstan` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +## Volume 4 (2017) + + +### Extreme value analysis and user defined probability functions in Stan + + +This notebook demonstrates how to implement user defined +probability functions in Stan language. As an example I use the +generalized Pareto distribution (GPD) to model geomagnetic storm data +from the World Data Center for Geomagnetism. + + +[View](case-studies/gpareto_functions.html) (HTML) + +Author +: Aki Vehtari + +Keywords +: extreme value analysis, generalized Pareto distribution, user defined probability functions + +Source Repository +: [avehtari/BDA_R_demos/demos_rstan/gpareto_functions](https://github.com/avehtari/BDA_R_demos/tree/master/demos_rstan/gpareto_functions) +(GitHub) + +R Package Dependencies +: `rstan`, `bayesplot`, `loo`, `ggplot2`, `tidyr`, `dplyr`, `extraDistr`, `gridExtra` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Modelling Loss Curves in Insurance with RStan + + +Loss curves are a standard actuarial technique for helping +insurance companies assess the amount of reserve capital they need to +keep on hand to cover claims from a line of business. Claims made and +reported for a given accounting period are tracked separately over +time. This enables the use of historical patterns of claim development +to predict expected total claims for newer policies. + +We model the growth of the losses in each accounting period as an +increasing function of time, and use the model to estimate the +parameters which determine the shape and form of this growth. We also +use the sampler to estimate the values of the “ultimate loss ratio”, +i.e. the ratio of the total claims on an accounting period to the +total premium received to write those policies. We treat each +accounting period as a cohort. + + +[View](case-studies/losscurves_casestudy.html) (HTML) + +Author +: Mick Cooney + +Keywords +: actuarial science, loss curves, insurance, ultimate loss ratio, +hierarchical model + +Source Repository +: [kaybenleroll/stancasestudy_losscurves](https://github.com/kaybenleroll/stancasestudy_losscurves) +(GitHub) + +R Package Dependencies +: `rstan`, `bayesplot`, `tidyverse`, `scales`, `cowplot` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Splines in Stan + + +In this document, we discuss the implementation of splines in Stan. We +start by providing a brief introduction to splines and then explain +how they can be implemented in Stan. We also discuss a novel prior +that alleviates some of the practical challenges of spline models. + + +[View](case-studies/splines_in_stan.html) (HTML) + +Author +: Milad Kharratzadeh + +Keywords +: B-splines, piecewise regression, knots, priors + +Source Repository +: [milkha/Splines_in_Stan](https://github.com/milkha/Splines_in_Stan) +(GitHub) + +R Package Dependencies +: `rstan`, `splines` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data + + +This case study shows how to efficiently encode and compute an +Intrinsic Conditional Auto-Regressive (ICAR) model in Stan. +When data has a neighborhood structure, ICAR models provide spatial smoothing +by averaging measurements of directly adjoining regions. +The Besag, York, and Mollié (BYM) model is a Poisson GLM which +includes both an ICAR component and an ordinary +random-effects component for non-spatial heterogeneity. +We compare two variants of the BYM model and fit two datasets +taken from epidemiological studies over 56 and 700 regions, respectively. + + +[View](case-studies/icar_stan.html) (HTML) + +Author +: Mitzi Morris + +Keywords +: spatial modeling, CAR, ICAR, INLA, OpenBUGS, hierarchical models + +Source Repository +: [stan-dev/example-models](https://github.com/stan-dev/example-models/tree/master/knitr/car-iar-poisson) +(GitHub) + +R Package Dependencies +: `cmdstanr`, `ggplot2`, `broom`, `reshape2`, `dplyr`, `maptools`, `spdep`, `R-INLA`, `R2OpenBugs` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### The QR Decomposition for Regression Models + + +This case study reviews the QR decomposition, a technique for decorrelating +covariates and, consequently, the resulting posterior distribution in +regression models. + + +[View](case-studies/qr_regression.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, regression, RStan + +Source Repository +: [betanalpha/knitr_case_studies/qr_regression](https://github.com/betanalpha/knitr_case_studies/tree/master/qr_regression) +(GitHub) + +R Package Dependencies +: `rstan`, `knitr`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + +### Robust RStan Workflow + + +This case study demonstrates the recommended RStan workflow for +ensuring robust inferences with the default dynamic Hamiltonian +Monte Carlo algorithm. + + +[View](case-studies/rstan_workflow.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, RStan + +Source Repository +: [betanalpha/knitr_case_studies/rstan_workflow](https://github.com/betanalpha/knitr_case_studies/tree/master/rstan_workflow) +(GitHub) + +R Package Dependencies +: `rstan`, `knitr`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + +### Robust PyStan Workflow + + +This case study demonstrates the recommended PyStan workflow for +ensuring robust inferences with the default dynamic Hamiltonian +Monte Carlo algorithm. + + +[View](case-studies/pystan_workflow.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, PyStan + +Source Repository +: [betanalpha/jupyter_case_studies/pystan_workflow](https://github.com/betanalpha/jupyter_case_studies/tree/master/pystan_workflow) +(GitHub) + +Python Package Dependencies +: `rstan`, `pystan, pickle, numpy, md5`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Typical Sets and the Curse of Dimensionality + + This case study illustrates the so-called “curse of +dimensionality” using simple examples based on simulation to show that +all points are far away in high dimensions and that the mode is an +atypical draw from a multivariate normal. The information-theoretic +concept of typical set is illustrated with both discrete and +continuous cases, which show that probability mass is a product of +volume and density (or count and mass in the discrete case). It also +illustrates Monte Carlo methods and relates distance to the log +density of the normal distribution and the chi-squared distribution. + + +[View R version](case-studies/curse-dims.html) +(HTML) + +Authors +: Bob Carpenter + +Keywords +: probability mass, typical sets, concentration of measure, Monte Carlo methods + +Source Repository (R) +: [stan-dev/example-models/knitr/curse-dims](https://github.com/stan-dev/example-models/tree/master/knitr/curse-dims) +(GitHub) + +R Package Dependencies +: `ggplot2` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +[View Python version](case-studies/curse-dims-python.html) +(HTML) + +Author (Python translation) +: Aravind S (Python translation) + +Source Repository (Python) +: [Aravinds-ds/Stan-Code/python notebooks/curse_dims](https://github.com/Arvinds-ds/Stan-Code/tree/master/python%20notebooks/curse_dims) +(GitHub) + +Python Package Dependencies +: `numpy`, `scipy`, `pandas`, `matplotlib`, `collections`, `sys` + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Diagnosing Biased Inference with Divergences + +This case study discusses the subtleties of accurate Markov chain Monte Carlo +estimation and how divergences can be used to identify biased estimation in +practice. + +[View](case-studies/divergences_and_bias.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, RStan + +Source Repository +: [betanalpha/knitr_case_studies/divergences_and_bias](https://github.com/betanalpha/knitr_case_studies/tree/master/divergences_and_bias) +(GitHub) + +R Package Dependencies +: `rstan`, `knitr`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### Identifying Bayesian Mixture Models + +This case study discusses the common pathologies of Bayesian mixture models as +well as some strategies for identifying and overcoming them. + + +[View](case-studies/identifying_mixture_models.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, Hamiltonian Monte Carlo, mixture models, +multimodal models, RStan + +Source Repository +: [betanalpha/knitr_case_studies/identifying_mixture_models](https://github.com/betanalpha/knitr_case_studies/tree/master/identifying_mixture_models) +(GitHub) + +R Package Dependencies +: `rstan`, `knitr`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + +### How the Shape of a Weakly Informative Prior Affects Inferences + +This case study reviews the basics of weakly-informative priors and how the +choice of a specific shape of such a prior affects the resulting posterior +distribution. + + +[View](case-studies/weakly_informative_shapes.html) (HTML) + +Author +: Michael Betancourt + +Keywords +: Markov chain Monte Carlo, Hamiltonian Monte Carlo, priors, +weakly-informative priors, RStan + +Source Repository +: [betanalpha/knitr_case_studies/weakly_informative_shapes](https://github.com/betanalpha/knitr_case_studies/tree/master/weakly_informative_shapes) +(GitHub) + +R Package Dependencies +: `rstan`, `knitr`. + +License +: Code: BSD (3 clause), Text: CC BY-NC 4.0 + + + +## Volume 3 (2016) + +### Exact Sparse CAR Models in Stan + +This document details sparse exact conditional autoregressive (CAR) models in Stan as an extension of previous work on approximate sparse CAR models in Stan. Sparse representations seem to give order of magnitude efficiency gains, scaling better for large spatial data sets. + + +[View](case-studies/mbjoseph-CARStan.html) (HTML) + +Author +: Max Joseph + +Keywords +: conditional autoregressive (CAR), independent autoregressive (IAR), sparsity, spatial random effects, maps + +Source Repository +: [mbjoseph/CARstan](https://github.com/mbjoseph/CARstan) (GitHub) + +R Package Dependencies +: `rstan`, `dplyr`, `ggmcmc`, `knitr`, `maptools`, `rgeos`, `spdep`. + +License +: BSD (3 clause), CC-BY + + +### A Primer on Bayesian Multilevel Modeling using PyStan + +This case study replicates the analysis of home radon levels using +hierarchical models of [Lin, Gelman, Price, and Kurtz (1999)](http://www.stat.columbia.edu/~radon/credits.html). +It illustrates how to generalize linear regressions to hierarchical models with +group-level predictors and how to compare predictive inferences and +evaluate model fits. Along the way it shows how to get data into Stan +using pandas, how to sample using PyStan, and how to visualize the results +using Seaborn. + +[View](case-studies/radon.html) (HTML) + +Author +: Chris Fonnesbeck + +Keywords +: hierarchical/multilevel modeling, linear regression, model comparison, predictive inference, radon + +Source Repository +: [fonnesbeck/stan_workshop_2016](https://github.com/fonnesbeck/stan_workshop_2016) +(GitHub) + +Python Package Dependencies +: `pystan`, `numpy`, `pandas`, `matplotlib`, `seaborn` + +License +: Apache 2.0 (code), CC-BY 3 (text) + + +### The Impact of Reparameterization on Point Estimates + + When changing variables, a Jacobian adjustment needs to be +provided to account for the rate of change of the transform. Applying +the adjustment ensures that inferences that are based on expectations +over the posterior are invariant under reparameterizations. In +contrast, the posterior mode changes as a result of the +reparameterization. In this note, we use Stan to code a repeated +binary trial model parameterized by chance of success, along with its +reparameterization in terms of log odds in order to demonstrate the +effect of the Jacobian adjustment on the Bayesian posterior and the +posterior mode. We contrast the posterior mode to the maximum +likelihood estimate, which, like the Bayesian estimates, is invariant +under reparameterization. Along the way, we derive the logistic +distribution by transforming a uniformly distributed variable. + + +[View](case-studies/mle-params.html) (HTML) + +Author +: Bob Carpenter + +Keywords +: MLE, Bayesian posterior, reparameterization, Jacobian, binomial + +Source Repository +: [example-models/knitr/mle-params](https://github.com/stan-dev/example-models/tree/master/knitr/mle-params) +(GitHub) + +R Package Dependencies +: `rstan` + +License +: BSD (3 clause), CC-BY + + +### Hierarchical Two-Parameter Logistic Item Response Model + +This case study documents a Stan model for the two-parameter logistic model (2PL) with hierarchical priors. A brief simulation indicates that the Stan model successfully recovers the generating parameters. An example using a grade 12 science assessment is provided. + +[View](case-studies/hierarchical_2pl.html) (HTML) + +Author +: Daniel C. Furr + +Keywords +: education, item response theory, two-parameter logistic model, hierarchical priors + +Source Repository +: [example-models/education/hierarchical_2pl](https://github.com/stan-dev/example-models/tree/master/education/hierarchical_2pl) +(GitHub) + +R Package Dependencies +: `rstan`, `ggplot2`, `mirt` + +License +: BSD (3 clause), CC-BY + + +### Rating Scale and Generalized Rating Scale Models with Latent Regression + +This case study documents a Stan model for the rating scale model (RSM) and the generalized rating scale model (GRSM) with latent regression. The latent regression portion of the models may be restricted to an intercept only, yielding a standard RSM or GRSM. A brief simulation indicates that the Stan models successfully recover the generating parameters. An example using a survey of public perceptions of science and technology is provided. + +[View](case-studies/rsm_and_grsm.html) (HTML) + +Authors +: Daniel C. Furr + +Keywords +: education, item response theory, rating scale model, generalized rating scale model + +Source Repository +: [example-models/education/rsm_and_grsm](https://github.com/stan-dev/example-models/tree/master/education/rsm_and_grsm) +(GitHub) + +R Package Dependencies +: `rstan`, `edstan`, `ggplot2`, `ltm` + +License +: BSD (3 clause), CC-BY + + +### Partial Credit and Generalized Partial Credit Models with Latent Regression + +This case study documents a Stan model for the partial credit model (PCM) and the generalized partial credit model (GPCM) with latent regression. The latent regression portion of the models may be restricted to an intercept only, yielding a standard PCM or GPCM. A brief simulation indicates that the Stan models successfully recover the generating parameters. An example using the TIMSS 2011 mathematics assessment is provided + +[View](case-studies/pcm_and_gpcm.html) (HTML) + +Authors +: Daniel C. Furr + +Keywords +: education, item response theory, partial credit model, generalized partial credit model + +Source Repository +: [example-models/education/pcm_and_gpcm](https://github.com/stan-dev/example-models/tree/master/education/pcm_and_gpcm) +(GitHub) + +R Package Dependencies +: `rstan`, `edstan`, `ggplot2`, `TAM` + +License +: BSD (3 clause), CC-BY + + +### Rasch and Two-Parameter Logistic Item Response Models with Latent Regression + +This case study documents Stan models for the Rasch and two-parameter logistic models with latent regression. The latent regression portion of the models may be restricted to an intercept only, yielding standard versions of the models. Simulations indicate that the two models successfully recover generating parameters. An example using a grade 12 science assessment is provided. + +[View](case-studies/rasch_and_2pl.html) (HTML) + +Authors +: Daniel C. Furr + +Keywords +: education, item response theory, rasch model, two-parameter logistic model + +Source Repository +: [example-models/education/rasch_and_2pl.html](https://github.com/stan-dev/example-models/tree/master/education/rasch_and_2pl) +(GitHub) + +R Package Dependencies +: `rstan`, `edstan`, `ggplot2`, `TAM` + +License +: BSD (3 clause), CC-BY + + + +### Two-Parameter Logistic Item Response Model + +This tutorial introduces the R package edstan for estimating +two-parameter logistic item response models using Stan without knowing +the Stan language. Subsequently, the tutorial explains how the model +can be expressed in the Stan language and fit using the rstan +package. Specification of prior distributions and assessment of +convergence are discussed. Using the Stan language directly has the +advantage that it becomes quite easy to extend the model, and this is +demonstrated by adding a latent regression and differential item +functioning to the model. Posterior predictive model checking is also +demonstrated. + +[View](case-studies/tutorial_twopl.html) (HTML) + +Author +: Daniel C. Furr, Seung Yeon Lee, Joon-Ho Lee, and Sophia Rabe-Hesketh + +Keywords +: education, item response theory, two-parameter logistic model + +Source Repository +: [example-models/education/tutorial_twopl](https://github.com/stan-dev/example-models/tree/master/education/tutorial_twopl) +(GitHub) + +R Package Dependencies +: `rstan`, `reshape2`, `ggplot2`, `gridExtra`, `devtools`, `edstan` + +License +: BSD (3 clause), CC-BY + +### Cognitive Diagnosis Model: DINA model with independent attributes + +This case study documents a Stan model for the DINA model with independent attributes. A Simulation indicates that the Stan model successfully recovers the generating parameters and predicts respondents' attribute mastery. A Stan model with no structure of the attributes is also discussed and applied to the simulated data. An example using a subset of the fraction subtraction data is provided. + +[View](case-studies/dina_independent.html) (HTML) + +Author +: Seung Yeon Lee + +Keywords +: education, cognitive diagnosis model, diagnostic classification model, attribute mastery, DINA + +Source Repository +: [example-models/education/dina_independent](https://github.com/stan-dev/example-models/tree/master/education/dina_independent) +(GitHub) + +R Package Dependencies +: `rstan`, `ggplot2`, `CDM` + +License +: BSD (3 clause), CC-BY + + + +### Pooling with Hierarchical Models for Repeated Binary Trials + +This note illustrates the effects on posterior inference of +pooling data (aka sharing strength) across items for repeated binary +trial data. It provides Stan models and R code to fit and check +predictive models for three situations: (a) complete pooling, which +assumes each item is the same, (b) no pooling, which assumes the items +are unrelated, and (c) partial pooling, where the similarity among the +items is estimated. We consider two hierarchical models to estimate +the partial pooling, one with a beta prior on chance of success and +another with a normal prior on the log odds of success. The note +explains with working examples how to (i) fit models in RStan and plot +the results in R using ggplot2, (ii) estimate event probabilities, +(iii) evaluate posterior predictive densities to evaluate model +predictions on held-out data, (iv) rank items by chance of success, +(v) perform multiple comparisons in several settings, (vi) replicate +new data for posterior p-values, and (vii) perform graphical posterior +predictive checks. + +[View](case-studies/pool-binary-trials.html) (HTML) + +Author +: Bob Carpenter + +Keywords +: binary trials, pooling, hierarchical models, baseball, epidemiology, prediction, posterior predictive checks + +Source Repository +: [example-models/knitr/pool-binary-trials](https://github.com/stan-dev/example-models/tree/master/knitr/pool-binary-trials) +(GitHub) + +R Package Dependencies +: `rstan`, `ggplot2`, `rmarkdown` + +License +: BSD (3 clause), CC-BY + + +##### RStanARM version + +There is also a version of this case study in which all models are fit using the +RStanARM interface. Many of the visualizations are also created using RStanARM's plotting +functions. + + +[View RStanARM version](case-studies/pool-binary-trials-rstanarm.html) (HTML) + +Author +: Bob Carpenter, Jonah Gabry, Ben Goodrich + + +## Volume 2 (2015) + + +### Multiple Species-Site Occupancy Model + +This case study replicates the analysis and output graphs of +Dorazio et al. (2006) noisy-measurement occupancy model for multiple +species abundance of butterflies. Going beyond the paper, the +supercommunity assumptions are tested to show they are invariant to +sizing, and posterior predictive checks are provided. + +[View](case-studies/dorazio-royle-occupancy.html) (HTML) + +Author +: Bob Carpenter + +Keywords +: ecology, occupancy, species abundance, supercommunity, +posterior predictive check + +Source Repository +: [example-models/knitr/dorazio-royle-occupancy](https://github.com/stan-dev/example-models/tree/master/knitr/dorazio-royle-occupancy) +(GitHub) + +License +: BSD (3 clause), CC-BY + +R Package Dependencies +: `rstan`, `ggplot2`, `rmarkdown` + + +## Volume 1 (2014) + + +### Soil Carbon Modeling with RStan + +This case study provides ordinary differential equation-based +compartment models of soil carbon flux, with experimental data fitted +with unknown initial compartment balance and noisy CO2 +measurements. Results form Sierra and Müller’s (2014) soilR package +are replicated. + +[View](case-studies/soil-knit.html) (HTML) + +Author +: Bob Carpenter + +Keywords +: biogeochemistry, compartment ODE, soil carbon +respiration, incubation experiment + +Source Repository +: [soil-metamodel/stan/soil-knit](https://github.com/soil-metamodel/stan/tree/master/soil-incubation) +(GitHub) + +License +: BSD (3 clause), CC-BY + +R Package Dependencies +: `rstan`, `ggplot2`, `rmarkdown` + +## Contributing Case Studies + +To contribute a case study, please contact us through the +[Stan Forums](https://discourse.mc-stan.org/). We require + +* a documented, reproducible example with narrative documentation +(e.g., knitr or Jupyter with software/compiler +versions noted and seeds fixed) and + +* an open-source code license (preferably BSD or GPL +for code, Creative Commons for text); authors retain all copyright. diff --git a/z2/learn-stan/index.qmd b/z2/learn-stan/index.qmd new file mode 100644 index 00000000..ba03f624 --- /dev/null +++ b/z2/learn-stan/index.qmd @@ -0,0 +1,8 @@ +--- +title: "Examples and Tutorials" +--- + +Members of the Stan community have contributed tutorials, case studies, and field guides. + + + diff --git a/z2/quarto_styles.css b/z2/quarto_styles.css new file mode 100644 index 00000000..2e8ab8f4 --- /dev/null +++ b/z2/quarto_styles.css @@ -0,0 +1,416 @@ +/* css styles */ +:root{ + --old-quarto-body-bg: #FEFBF2; + --quarto-body-bg: #FFFDF9; + --quarto-body-color: #052744; + --quarto-text-muted: #053A42; + --bs-body-bg: #FEFBF2; + --bs-body-color: #052744; + --bs-dark: #052744; + --bs-dark-warm: #053A42; 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+} + +pre.sourceCode { + white-space: inherit; + margin: 0; + padding: 0; + color: var(--bs-dark-warm); + background-color: var(--palest-beige); +} + +#quarto-appendix { + color: var(--bs-dark-warm); + background-color: var(--palest-beige); +} + +.align-equations { + text-align: left; + margin-left: 2em; /* Adjust as needed */ +} + +.book .book-body .page-wrapper .page-inner section.normal pre { + font-family: "Lucida Console", Monaco, Menlo, monospaced; + margin-left: 2em; + margin-below: 0.6em; + margin-above: -0.3em; + padding: 0; +} + +.sidebar.sidebar-navigation>* { + padding-top: 0em; +} + + +#quarto-sidebar > div.sidebar-menu-container > ul > li:nth-child(1) > div > a > span { + font-weight: bold; + font-style: italic; + font-size: 1.3em; + color: var(--aquatic-blue); + margin-above: -20em; + padding: 0; +} diff --git a/z2/quickstart.qmd b/z2/quickstart.qmd new file mode 100644 index 00000000..f14e205f --- /dev/null +++ b/z2/quickstart.qmd @@ -0,0 +1,18 @@ +--- +title: "Getting Started" +toc: false +--- + +Stan logo + +### Hello World + + +Run Stan's demo model: example/bernoulli.stan + + +- compile Stan model to executable program +- run program + data +- get output summary +- do downstream analysis + diff --git a/z2/stan.xml b/z2/stan.xml new file mode 100644 index 00000000..3b03a36c --- /dev/null +++ b/z2/stan.xml @@ -0,0 +1,150 @@ + + + + + + break + continue + else + for + if + in + return + while + profile + + + reject + print + integrate_ode + integrate_ode_bdf + integrate_ode_rk45 + algebra_solver + + + lower + upper + offset + multiplier + int + real + vector + complex + ordered + positive_ordered + simplex + unit_vector + row_vector + matrix + array + tuple + cholesky_factor_corr + cholesky_factor_cov + corr_matrix + cov_matrix + void + complex_matrix + complex_vector + complex_row_vector + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 4c5d3d29ce71abc647f07f74fcd458e468859536 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Sat, 6 Apr 2024 17:47:13 -0400 Subject: [PATCH 7/8] cleanup --- .../case-studies/gpareto_functions.html | 980 --------- users/documentation/index.md.bak | 176 -- zissou/_quarto.yml | 154 -- zissou/about.qmd | 81 - zissou/all.bib | 1760 ----------------- zissou/community.qmd | 12 - zissou/discussions.qmd | 12 - zissou/docs.qmd | 14 - zissou/download.qmd | 13 - zissou/img/discussion_button.svg | 50 - zissou/img/documentation_button.svg | 51 - zissou/img/download_button.svg | 32 - zissou/img/logo_tm.png | Bin 21065 -> 0 bytes zissou/img/logo_tm.svg | 6 - zissou/img/numfocus.png | Bin 13662 -> 0 bytes zissou/index.qmd | 32 - zissou/install.qmd | 12 - zissou/js.html | 19 - zissou/styles.css | 373 ---- zissou/support.qmd | 13 - 20 files changed, 3790 deletions(-) delete mode 100644 users/documentation/case-studies/gpareto_functions.html delete mode 100644 users/documentation/index.md.bak delete mode 100644 zissou/_quarto.yml delete mode 100644 zissou/about.qmd delete mode 100644 zissou/all.bib delete mode 100644 zissou/community.qmd delete mode 100644 zissou/discussions.qmd delete mode 100644 zissou/docs.qmd delete mode 100644 zissou/download.qmd delete mode 100644 zissou/img/discussion_button.svg delete mode 100644 zissou/img/documentation_button.svg delete mode 100644 zissou/img/download_button.svg delete mode 100644 zissou/img/logo_tm.png delete mode 100644 zissou/img/logo_tm.svg delete mode 100644 zissou/img/numfocus.png delete mode 100644 zissou/index.qmd delete mode 100644 zissou/install.qmd delete mode 100644 zissou/js.html delete mode 100644 zissou/styles.css delete mode 100644 zissou/support.qmd diff --git a/users/documentation/case-studies/gpareto_functions.html b/users/documentation/case-studies/gpareto_functions.html deleted file mode 100644 index 32fdd7bc..00000000 --- a/users/documentation/case-studies/gpareto_functions.html +++ /dev/null @@ -1,980 +0,0 @@ - - - - - - - - - - - - - - - -Extreme value analysis and user defined probability functions in Stan - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -