Bayesian Applied Regression Modeling via Stan

By Jonah Gabry, Ben Goodrich. 2016

858 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/43f34295adf6b4acffd5ca31eddde131cbeab4db

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Citations in the corpus

Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (with Discussion) (2019, methodology): “Since the marginal posterior distributions might not have ﬁnite mean and variance, for example, the popular rstanarm package (Stan Development Team, 2018a) reports median and median absolute deviation (MAD) instead of mean and standard error.”
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC (2015, methodology): “…is not automatic when writing custom Stan programs, we can create implementations that are automatic for users of our new rstanarm R package (Gabry and Goodrich, 2016). rstanarm provides a high-level interface to Stan that enables the user to specify many of the most common applied Bayesian…”
brms: An R Package for Bayesian Multilevel Models Using Stan (2017, methodology): “2015) and MCMCglmm (Hadfield 2010), which are possibly the most general and widely applied R packages for MLMs, as well as rstanarm (Gabry and Goodrich 2017) and rethinking (McElreath 2016), which are both based on Stan.”
Indices of Effect Existence and Significance in the Bayesian Framework (2019, methodology): “Computations of Bayesian models were done using the rstanarm package (Goodrich et al., 2019), a wrapper for Stan probabilistic language (Carpenter et al.”
R-squared for Bayesian Regression Models (2019, methodology): “Our motivation is the rstanarm R package (Gabry and Goodrich, 2017) for fitting applied regression models using Stan (Stan Development Team, 2017).”
QED: A Framework and Dataset for Explanations in Question Answering (2020, methodology): “To control for the correlations induced by the rater and question groups, we fit a generalized linear mixed model (GLMM) using the rstanarm R package (Goodrich et al., 2020).”
An Introduction to Mixed Models for Experimental Psychology (2019, unknown): “One further alternative for addressing convergence problems is to switch to Bayesian estimation (Gelman et al., 2013), for example as implemented in packages rstanarm (function stan_lmer() ; Gabry & Goodrich, 2016), blme (Chung et al., 2013), or MCMCglmm (Hadﬁeld, 2010).”
Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math (2020, unknown): “3 (79), using the rstanarm package (80).”
Advanced Bayesian Multilevel Modeling with the R Package brms (2017, background): “…|| operator in lme4 is that it only splits up terms so that columns of the design matrix originating from the same term are still modeled as correlated (e.g., when coding a categorical predictor; see the mixed function of the afex package by Singmann et al. (2015) for a way to avoid this behavior).”
bridgesampling: An R Package for Estimating Normalizing Constants (2017, unknown): “Assisted by the rstan package ( Stan Development Team 2016a), this interface allows users to skip steps (b)-(d) above.”
blavaan: Bayesian structural equation models via parameter expansion (2015, methodology): “For example, package rstanarm (Gabry and Goodrich 2016) allows users to estimate generalized linear mixed models via Stan, using lme4 (Bates, Mächler, Bolker, and Walker 2015) syntax.”
Get ready! High urgency reduces beta band cortico-muscular coherence during motor preparation (2024, unknown): “We follow up our traditional data analysis approach with Bayesian linear mixed model analyses using the stan_lmer function from the rstanarm package (Goodrich et al., 2020).”