The Exponentially Modified Gaussian Distribution
The Generalized Extreme Value Distribution
The (Multivariate) Logistic Normal Distribution
Extract Variance and Correlation Components
Add compiled 'rstan' models to 'brmsfit' objects
Extract posterior samples for use with the 'coda' package
Compute a Bayesian version of R-squared for regression models
Log Marginal Likelihood via Bridge Sampling
Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models
Run the same 'brms' model on multiple datasets
Special Family Functions for 'brms' Models
Class 'brmsfit' of models fitted with the 'brms' package
Linear and Non-linear formulas in 'brms'
Set up a model formula for use in 'brms'
Spatial conditional autoregressive (CAR) structures
Compare Information Criteria of Different Models
Display Conditional Effects of Predictors
Extract Control Parameters of the NUTS Sampler
(Deprecated) Correlation structure classes for the 'brms' package
(Defunct) Basic Bayesian Structural Time Series
(Deprecated) Spatial conditional autoregressive (CAR) structures
(Deprecated) Compound Symmetry (COSY) Correlation Structure
(Deprecated) Fixed user-defined covariance matrices
(Deprecated) Spatial simultaneous autoregressive (SAR) structures
Category Specific Predictors in 'brms' Models
Extract Diagnostic Quantities of 'brms' Models
Fixed residual correlation (FCOR) structures
Expected Values of the Posterior Predictive Distribution
Projection Predictive Variable Selection: Get Reference Model
Checks if argument is a 'brmsfit' object
Checks if argument is a 'brmsfit_multiple' object
Checks if argument is a 'brmsformula' object
Checks if argument is a 'brmsprior' object
Checks if argument is a 'brmsterms' object
Check if argument is a correlation structure
Checks if argument is a 'mvbrmsformula' object
Checks if argument is a 'mvbrmsterms' object
Efficient approximate leave-one-out cross-validation (LOO)
Compute a LOO-adjusted R-squared for regression models
Model averaging via stacking or pseudo-BMA weighting.
Moment matching for efficient approximate leave-one-out cross-validation
Efficient approximate leave-one-out cross-validation (LOO) using subsampling
Predictors with Measurement Error in 'brms' Models
Predictors with Missing Values in 'brms' Models
Set up multi-membership grouping terms in 'brms'
Bind response variables in multivariate models
Set up a multivariate model formula for use in 'brms'
(Deprecated) Number of Posterior Samples
Create a matrix of output plots from a 'brmsfit' object
Posterior Model Probabilities from Marginal Likelihoods
Posterior draws of parameters averaged across models
Draws from the Expected Value of the Posterior Predictive Distribution
Draws from the Posterior Predictive Distribution
Posterior predictive draws averaged across models
Posterior Predictive Checks for 'brmsfit' Objects
Posterior Probabilities of Mixture Component Memberships
Print a summary for a fitted model represented by a 'brmsfit' object
Extract Priors of a Bayesian Model Fitted with 'brms'
Recompile Stan models in 'brmsfit' objects
Compute exact cross-validation for problematic observations
Posterior Draws of Residuals/Predictive Errors
Spatial simultaneous autoregressive (SAR) structures
Create a summary of a fitted model represented by a 'brmsfit' object
(Deprecated) Black Theme for 'ggplot2' Graphics
Default 'bayesplot' Theme for 'ggplot2' Graphics
Update 'brms' models based on multiple data sets
Covariance and Correlation Matrix of Population-Level Effects
Widely Applicable Information Criterion (WAIC)