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.

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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 (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.

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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 (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 CmdStanPyinterface and plotnine, a Python implementation of a grammar of graphics based on ggplot2.

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Authors: Mitzi Morris
Keywords: Python, CmdStanPy, plotnine, hierarchical/multilevel modeling, linear regression, posterior predictive checks, radon
Source Repository: example-models/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.

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Authors: Brian Ward, Bob Carpenter, and David Barmherzig
Keywords: image reconstruction, phase retrieval, Fourier transforms, deconvolution
Source Repository: 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.

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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 (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.

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Authors: Charles Margossian and Andrew Gelman
Keywords: ordinary differential equations, multimodality, classical mechanics
Source Repository: example-models/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.

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Authors: Ben Bales
Keywords: Hidden Markov Models, HMMs, cmdstanr, Stan programming
Source Repository: example-models/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.

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Authors: Cristina Barber, Andrii Zaiats, Cara Applestein and T.Trevor Caughlin
Keywords: plants, neighbor interactions, sparse matrix, segment function
Source Repository: 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 “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.

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Author: Hyunji Moon, Jungin Choi
Keywords: Hierarchical Gaussian process, Bayesian workflow
Source Repository: example-models/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.

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Authors: Ben Bales, Sebastian Weber
Keywords: ordinary differential equations, cmdstanr, Stan programming
Source Repository: example-models/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.

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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 (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

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Author: Ben Bales
Keywords: within-chain parallel computation, cmdstanr, Stan programming
Source Repository: example-models/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 and CmdStanPy.

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Author: Mitzi Morris
Keywords: Jupyter, Google Colab, teaching Stan, online classroom, cloud computing
Source Repository: example-models/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.

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Author: JoonHo Lee, Avi Feller and Sophia Rabe-Hesketh
Keywords: causal inference, completely randomized experiments
Source Repository: example-models/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.

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Author: Imad Ali
Keywords: hidden markov models, sports
Source Repository: 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.

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Author: Andrew Gelman
Keywords: nonlinear regression, sports
Source Repository: example-models/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.

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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 (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.

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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 (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.

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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.

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Author: Lu Zhang
Keywords: Gaussian process, nearest neighbor Gaussian process, spatial models, latent process, regression
Source Repository: 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.

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Author: Aki Vehtari
Keywords: extreme value analysis, generalized Pareto distribution, user defined probability functions
Source Repository: avehtari/BDA_R_demos/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.

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Author: Mick Cooney
Keywords: actuarial science, loss curves, insurance, ultimate loss ratio, hierarchical model
Source Repository: 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.

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Author: Milad Kharratzadeh
Keywords: B-splines, piecewise regression, knots, priors
Source Repository: 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.

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Author: Mitzi Morris
Keywords: spatial modeling, CAR, ICAR, INLA, OpenBUGS, hierarchical models
Source Repository: stan-dev/example-models (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.

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Author: Michael Betancourt
Keywords: Markov chain Monte Carlo, regression, RStan
Source Repository: betanalpha/knitr_case_studies/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.

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Author: Michael Betancourt
Keywords: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, RStan
Source Repository: betanalpha/knitr_case_studies/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.

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Author: Michael Betancourt
Keywords: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, PyStan
Source Repository: betanalpha/jupyter_case_studies/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.

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Authors: Bob Carpenter
Keywords: probability mass, typical sets, concentration of measure, Monte Carlo methods
Source Repository (R): stan-dev/example-models/knitr/curse-dims (GitHub)
R Package Dependencies: ggplot2
License: Code: BSD (3 clause), Text: CC BY-NC 4.0

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Author (Python translation): Aravind S (Python translation)
Source Repository (Python): Aravinds-ds/Stan-Code/python notebooks/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.

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Author: Michael Betancourt
Keywords: Markov chain Monte Carlo, Hamiltonian Monte Carlo, divergences, RStan
Source Repository: betanalpha/knitr_case_studies/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.

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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 (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.

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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 (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.

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Author: Max Joseph
Keywords: conditional autoregressive (CAR), independent autoregressive (IAR), sparsity, spatial random effects, maps
Source Repository: 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). 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.

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Author: Chris Fonnesbeck
Keywords: hierarchical/multilevel modeling, linear regression, model comparison, predictive inference, radon
Source Repository: 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.

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Author: Bob Carpenter
Keywords: MLE, Bayesian posterior, reparameterization, Jacobian, binomial
Source Repository: example-models/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.

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Author: Daniel C. Furr
Keywords: education, item response theory, two-parameter logistic model, hierarchical priors
Source Repository: example-models/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.

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Authors: Daniel C. Furr
Keywords: education, item response theory, rating scale model, generalized rating scale model
Source Repository: example-models/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

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Authors: Daniel C. Furr
Keywords: education, item response theory, partial credit model, generalized partial credit model
Source Repository: example-models/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.

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Authors: Daniel C. Furr
Keywords: education, item response theory, rasch model, two-parameter logistic model
Source Repository: example-models/education/rasch_and_2pl.html (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.

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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 (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.

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Author: Seung Yeon Lee
Keywords: education, cognitive diagnosis model, diagnostic classification model, attribute mastery, DINA
Source Repository: example-models/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.

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Author: Bob Carpenter
Keywords: binary trials, pooling, hierarchical models, baseball, epidemiology, prediction, posterior predictive checks
Source Repository: example-models/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(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.

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Author: Bob Carpenter
Keywords: ecology, occupancy, species abundance, supercommunity, posterior predictive check
Source Repository: example-models/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.

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Author: Bob Carpenter
Keywords: biogeochemistry, compartment ODE, soil carbon respiration, incubation experiment
Source Repository: soil-metamodel/stan/soil-knit (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. 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.