RstanarmEdit
rstanarm is an R package that brings Bayesian regression modeling to applied researchers by building on the Stan probabilistic programming language. It provides a formula-based interface that mirrors familiar tools like Generalized linear model fitting in R, but returns full posterior distributions for parameters and predictions rather than single point estimates. By tying the modeling workflow to Stan and its efficient samplers, rstanarm makes principled uncertainty quantification accessible to practitioners in economics, political science, psychology, epidemiology, and beyond. The project sits at the intersection of open-source statistical computing and transparent, replicable science, aligning well with standards that emphasize explicit assumptions and reproducible workflows. For context, it sits within the broader Stan ecosystem, alongside interfaces like rstan and related tools for model evaluation and visualization such as bayesplot.
Overview and history
rstanarm was developed to bring the reliability and coherence of Bayesian inference to applied users who prefer not to write Stan code from scratch. The package enables fitting a wide range of models using a familiar, R-like syntax while delegating the heavy lifting to the underlying Stan platform. This design reflects a pragmatic philosophy: keep the interface approachable for routine analyses, provide principled uncertainty via the posterior distribution, and foster reproducibility through explicit priors and traceable model specifications. The project has been maintained by contributors who are part of the broader ecosystem around Bayesian statistics and Stan, with links to core ideas and implementations in the Stan community, including references to the language itself and its supporters such as Stan (software) and the developers who maintain the Stan project.
Features and capabilities
Formula-based modeling interface: users specify models with a familiar syntax, and rstanarm translates the specification into Stan code that is compiled and run to sample from the posterior. This makes Bayesian methods more approachable for practitioners who already know how to write models with Generalized linear model-style formulas.
Wide family support and flexiblity: the package supports linear and generalized linear models and their hierarchical variants, enabling fits for continuous, binary, count, and other outcomes. It is designed to accommodate multilevel structures and partial pooling through Hierarchical model concepts, which helps stabilize estimates in small samples or complex designs. See how these ideas relate to other modeling frameworks in the encyclopedia, such as Generalized linear model and Hierarchical model.
Priors and inference: rstanarm emphasizes explicit prior specification and provides sensible defaults in the form of weakly informative priors that aim to regularize estimates without being overly restrictive. Priors can be tailored for intercepts and coefficients, and users can perform prior-posterior checks to understand how assumptions influence the results. The concept of priors is central to Bayesian inference and is discussed in depth in articles on Prior (Bayesian) and Posterior distribution.
Inference output and diagnostics: after fitting, users obtain the full posterior distribution for parameters, as well as posterior predictive distributions for new data. Diagnostic tools and visualization help assess convergence and fit, with ties to tools like PSIS-LOO and other Model evaluation techniques.
Model comparison and predictive checks: rstanarm supports information criteria and cross-validation approaches common in Bayesian practice, enabling comparisons between competing specifications and assessments of predictive accuracy. See Leave-one-out cross-validation and WAIC for related ideas.
Interoperability and ecosystem: outputs can be summarized, visualized, and integrated with other R workflows. The package aligns with other Bayesian tooling in the R ecosystem, including bayesplot for diagnostic plots and posterior for manipulating posterior draws.
Modeling approach and interpretation
rstanarm centers the Bayesian paradigm: uncertainty about parameters is expressed through posterior distributions that are updated as data are observed. This yields interval statements about parameters and predictions that have probabilistic interpretation, rather than relying solely on p-values or point estimates. The use of priors—ranging from weakly informative to more informative ones—provides a principled way to incorporate domain knowledge and to regularize estimates in the face of limited data or complex models. The emphasis on transparency and explicit assumptions aligns with best practices in rigorous empirical work.
Priors and sensitivity: a core feature is the clear treatment of priors and the ability to conduct prior sensitivity analyses. Users can inspect how different prior choices affect the posterior, which supports robust inference in settings where data alone may be insufficient to identify parameters cleanly. See Prior (Bayesian) and Posterior distribution for baseline concepts.
Regularization through priors: weakly informative priors help prevent overfitting and stabilize estimates in small samples or highly parameterized models. This is often contrasted with noninformative priors, which can be theoretically appealing but practically problematic because they may lead to improper posteriors or unstable inferences.
Interpretation of results: the posterior summaries produced by rstanarm enable direct probabilistic statements about effects and predictions, which some researchers view as more natural and informative than single-number point estimates. See Bayesian statistics for broader context.
Computation, performance, and practical use
Stan backend and MCMC: computation runs through the Stan sampling engine, typically using the No-U-Turn Sampler (NUTS). This yields robust posterior samples but requires model compilation and can be more computationally intensive than some frequentist alternatives. The trade-off is richer uncertainty quantification and better model diagnostics, especially in complex models. For background on the sampling method, see No-U-Turn Sampler and Markov chain Monte Carlo.
Model fitting workflow: rstanarm is designed to fit models with relatively few lines of code, extract posterior summaries, and perform posterior predictive checks. It is particularly useful for applied researchers who want to move quickly from model specification to interpretation without writing custom Stan code.
Limitations and caveats: while the interface is convenient, users should be mindful of priors, model misspecification, and computational cost in large or highly hierarchical models. The Bayesian workflow does not absolve researchers from performing rigorous model checking and sensitivity analyses, which are essential parts of credible inference.
Controversies and debates
The practical adoption of Bayesian methods via tools like rstanarm sits within ongoing debates about statistical practice. Proponents emphasize transparency, full uncertainty quantification, and the ability to incorporate prior knowledge in a principled way. Critics often argue that Bayesian analyses can be unduly swayed by priors or computational choices, especially when defaults are used without scrutiny.
Priors and subjectivity: a common critique is that priors encode subjective beliefs that can influence results. Supporters counter that all statistical analyses involve assumptions, and priors in a transparent Bayesian workflow are explicit and testable. They advocate for prior sensitivity analyses and for reporting prior and posterior perspectives side by side. See Prior (Bayesian) and Posterior distribution.
Default priors and scientific neutrality: some observers worry that default weakly informative priors may still bias results in subtle ways. Proponents argue that default choices are chosen to be broadly reasonable, to improve identifiability, and to reflect modest knowledge in the absence of strong information. They also point to the ability to customize priors as a corrective, and to posterior predictive checks as a means of verifying that inferences align with observed data.
Computational costs and accessibility: Bayesian methods, especially with hierarchical models, require more computational resources and time than some traditional methods. This has led to debates about when the added burden is justified. Advocates emphasize that the extra cost pays off in richer uncertainty quantification and more reliable decision guidance, particularly for small samples or high-stakes inferences.
Woke criticisms and practical response: some critics allege that statistical practice is biased by broader ideological preferences embedded in priors or model choices. The defense is that Bayesian modeling makes assumptions explicit and subject to scrutiny, and that robust analysis includes sensitivity checks and alternative specifications. In practice, rstanarm’s design supports these checks and encourages transparent reporting, which many researchers see as a counterweight to selective interpretation.
Reproducibility and policy impact: Bayesian workflows with open data, shared code, and fully documented priors tend to improve replicability and accountability, especially in policy-relevant research. Critics of statistical approaches that rely on opaque methods may resist, but the argument in favor of tools like rstanarm is that they make uncertainty and assumptions auditable rather than hidden.