Since its foundation, several people have blogged about my R package **brms**, which allows to fit Bayesian generalized non-linear multilevel models using Stan. This post is intended to provide links to those blog posts.

## List of blog posts

- Statistical Rethinking with brms, ggplot2, and the tidyverse by Solomon Kurz
- Diffusion/Wiener Model Analysis with brms – Part III: Hypothesis Tests of Parameter Estimates by Henrik Singmann
- Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models by Kristoffer Magnusson
- Extracting and visualizing tidy draws from brms models by Matthew Kay
- Use domain knowledge to review prior distributions by Markus Gesmann
- Hierarchical loss reserving with growth curves using brms by Markus Gesmann
- A series of tutorials how to run the brms package by Rens van de Schoot
- Models are about what changes, and what doesn’t by Markus Gesmann
- R functions for Bayesian Model Statistics and Summaries #rstats #stan #brms by Daniel Lüdecke
- Visualizing insect count data — a zero-inflated poisson model by Andrew MacDonald
- Fitting GAMs with brms: part 1 a simple GAM by Gavin Simpson
- Mixed effects models: Is it time to go Bayesian by default? by Michael Frank
- Easily made fitted and predicted values made easy by Andrew MacDonald
- How to compute Bayes factors using lm, lmer, BayesFactor, brms, and JAGS/stan/pymc3 by Jonas Kristoffer Lindelov
- Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms by Kristoffer Magnusson
- MRP Using brms and tidybayes by Tim Mastny
- PK/PD reserving models by Markus Gesmann
- Hierarchical compartmental reserving models by Markus Gesmann
- Diffusion/Wiener Model Analysis with brms – Part II: Model Diagnostics and Model Fit by Henrik Singmann
- Bayesian SEM with brms by Jarrett Byrnes
- Diffusion/Wiener Model Analysis with brms – Part I: Introduction and Estimation by Henrik Singmann
- Bayesian Decision Theory Made Ridiculously Simple by Justin Silverman
- Bayesian Estimation of Signal Detection Models, Part 4 by Matti Vuorre
- Bayesian Estimation of Signal Detection Models, Part 3 by Matti Vuorre
- Bayesian Estimation of Signal Detection Models, Part 2 by Matti Vuorre
- Bayesian Estimation of Signal Detection Models, Part 1 by Matti Vuorre
- Three methods for computing the intra-class correlation in multilevel logistic regression by Ladislas Nalborczyk
- More support for Bayesian analysis in the sj!-packages #rstats #rstan #brms by Daniel Luedecke
- R packages interfacing with Stan: brms posted on Andrew Gelmans blog
- Bayesian mixed effects (aka multi-level) ordinal regression models with brms by Kevin Stadler
- Bayes factors with brms by Matti Vuorre
- How to Compare Two Groups with Robust Bayesian Estimation Using R, Stan and brms by Matti Vuorre
- Better forest plots from meta-analytic models estimated with brms by Matti Vuorre
- Meta-analysis is a special case of Bayesian multilevel modeling by Matti Vuorre
- brms continuous its streak by Wayne Folta
- brms 0.8 adds non-linear regression by Wayne Folta
- R Users Will Now Inevitably Become Bayesians by Wayne Folta
- Bayesian regression models using Stan in R by Markus Gesmann