Jonah Sol Gabry
Software and papers
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Stan
Stan is a statistical modeling language along with inference algorithms for full
Bayesian inference, approximate Bayesian inference, and penalized maximum likelihood
estimation. Stan is implemented in C++ but we provide interfaces for the
command line, R and Python (and more).
R packages
rstan
The rstan package is the R interface to Stan.
website |
CRAN |
source code on GitHub
rstanarm
The rstanarm package is for Bayesian applied regression modeling (ARM) via Stan.
It is an appendage to the rstan package that enables some of the
most common applied regression models to be estimated using Markov Chain
Monte Carlo, variational approximations to the posterior distribution, or
optimization. The rstanarm package allows these models to be
specified using the customary R modeling syntax (e.g., like that of
glm with a formula and a data.frame).
website |
CRAN |
source code on GitHub
shinystan
The shinystan R package provides a graphical user interface providing
interactive visual and numerical summaries of model parameters and convergence
diagnostics for Bayesian models estimated using MCMC.
website |
CRAN |
source code on GitHub
bayesplot
The bayesplot R package provides a library of plotting functions for use
after fitting Bayesian models (typically with MCMC).
website |
CRAN |
source code on GitHub
A paper about bayesplot and visualization in the Bayesian workflow more generally:
- Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman. (2017). Visualization in Bayesian workflow. arXiv preprint arxiv:1709.01449
loo
The loo R package is for efficient approximate leave-one-out cross-validation for Bayesian models.
website |
CRAN |
source code on GitHub
The package implements the methods described in these papers with Aki and Andrew:
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Aki Vehtari, Andrew Gelman, and Jonah Gabry. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), pp 1413-1432. (link to published version) | arXiv preprint arxiv:1507.04544
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Aki Vehtari, Andrew Gelman, and Jonah Gabry. (2016). Pareto smoothed importance sampling. arXiv preprint arxiv:1507.02646
rstantools
The rstantools R package provides tools for developing R packages interfacing with Stan.
website |
CRAN |
source code on GitHub
posterior
The posterior R package provides efficient conversion between many different useful formats
of draws (samples) from posterior or prior distributions, consistent methods for operations commonly
performed on draws, various summaries of draws in convenient formats, and lightweight implementations
of state of the art posterior inference diagnostics.
website |
CRAN |
source code on GitHub
cmdstanr
The cmdstanr R package provides an R interface to CmdStan.
website | source code on GitHub
Papers
- [2024] Pareto Smoothed Importance Sampling. Journal of Machine Learning Research (Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry)
- [2022] Fast methods for posterior inference of two-group normal-normal models. Bayesian Analysis (Philip Greengard, Jeremy Hoskins, Charles C. Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari)
- [2020] Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models. Computational Statistics (Paul Bürkner, Jonah Gabry, Aki Vehtari)
- [2020] Approximate leave-future-out cross-validation for Bayesian time series models. Journal of Statistical Computation and Simulation (Paul Bürkner, Jonah Gabry, Aki Vehtari)
- [2020] Bayesian hierarchical weighting adjustment and survey inference. Survey Methodology (Yajuan Si, Rob Trangucci, Jonah Gabry, Andrew Gelman)
- [2019] Visualization in Bayesian workflow (with discussion). Journal of the Royal Statistical Society A. (Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, Andrew Gelman)
- [2018] R-squared for Bayesian regression models. The American Statistician. (Andrew Gelman, Ben Goodrich, Jonah Gabry, Aki Vehtari)
- [2018] User-friendly Bayesian regression modeling. The Quantitative Methods for Psychology. (Chelsea Muth, Zita Oravecz, Jonah Gabry)
- [2018] Bayesian hierarchical models for predicting individual performance in soccer. Journal of Quantitative Analysis in Sports. (Leonardo Egidi and Jonah Gabry)
- [2017] Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. (Aki Vehtari, Andrew Gelman, Jonah Gabry)
- [2017] Antiemetic prophylaxis as a marker of health care disparities in the National Anesthesia Clinical Outcomes Registry. Anesthesia & Analgesia. (Michael Andreae, Jonah Gabry, Ben Goodrich, Robert White, Charles Hall)
- [2017] A pragmatic trial to improve adherence with scheduled appointments in an inner-city pain clinic by human phone calls in the patient’s preferred language. Journal of Clinical Anesthesia. (Michael Andreae, Singh Nair, Jonah Gabry, Ben Goodrich, Charles Hall, Naum Shaparin)
Preprints:
- Preoperative Exposure to Fine Particulate Matter and Risk of Postoperative Complications: A Single Center Observational Cohort Bayesian Analysis (John F Pearson, Cameron Jacobson, Calvin Riss, Matthew Strickland, Longyin Lee, Neng Wan, Tabitha M. Benney, Nathan L Pace, Ben Goodrich, Jonah Gabry, Cade Kartchner, Michael H Andreae)
- Multilevel Regression and Poststratification Interface: Application to Track Community-level COVID-19 Viral Transmission (Yajuan Si, Toan Tran, Jonah Gabry, Mitzi Morris, Andrew Gelman)
- Baysian workflow (Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, Martin Modrák)
- Using multilevel regression and poststratification to estimate dynamic public opinion. (Andrew Gelman, Jeffrey Lax, Justin Phillips, Jonah Gabry, Robert Trangucci)
Tutorials and educational materials
A selection of tutorial vignettes on practical topics in Bayesian data analysis.
Visualization
Bayesian regression modeling with rstanarm
- GLMs for Binary/Binomial Data
- GLMs for Continuous Data
- GLMs for Count Data
- Generalized (Non-)Linear Models with Group-Specific Terms
- Bayesian ANOVA Models
- Regularized Linear Models
- Ordinal Regression Models
- Hierarchical Partial Pooling for Repeated Binary Trials
- Modeling Rates/Proportions using Beta Regression
- Multilevel regression and poststratification
Predictive performance and model comparison
- Using the loo package (version >= 2.0.0)
- Bayesian Stacking and Pseudo-BMA weights
- Writing Stan programs for use with the loo package
- Leave-one-out cross-validation for non-factorizable models
- Approximate leave-future-out cross-validation for time series models
- Using Leave-one-out cross-validation for large data