brms

The Asymmetric Laplace Distribution

The Dirichlet Distribution

The Exponentially Modified Gaussian Distribution

The Frechet Distribution

The Generalized Extreme Value Distribution

Hurdle Distributions

The Inverse Gaussian Distribution

The Multivariate Normal Distribution

The Multivariate Student-t Distribution

R2-D2 Priors in brms

The Shifted Log Normal Distribution

The Skew-Normal Distribution

The Student-t Distribution

Extract Variance and Correlation Components

The von Mises Distribution

The Wiener Diffusion Model Distribution

Zero-Inflated Distributions

Add model fit criteria to model objects

Add model fit criteria to model objects

Additional Response Information

Set up AR(p) correlation structures

Set up ARMA(p,q) correlation structures

Extract posterior samples for use with the coda package

Autocorrelation structures

(Deprecated) Extract Autocorrelation Objects

Compute a Bayesian version of R-squared for regression models

Bayes Factors from Marginal Likelihoods

Log Marginal Likelihood via Bridge Sampling

Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models

Run the same brms model on multiple datasets

Bayesian Regression Models using 'Stan'

Special Family Functions for brms Models

Class brmsfit of models fitted with the brms package

Check if cached fit can be used.

Set up a model formula for use in brms

Linear and Non-linear formulas in brms

Descriptions of brmshypothesis Objects

Parse Formulas of brms Models

Spatial conditional autoregressive (CAR) structures

Extract Model Coefficients

Combine Models fitted with brms

Compare Information Criteria of Different Models

Display Conditional Effects of Predictors

Display Smooth Terms

Extract Control Parameters of the NUTS Sampler

(Deprecated) AR(p) correlation structure

(Deprecated) ARMA(p,q) correlation structure

(Defunct) ARR correlation structure

(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) MA(q) correlation structure

(Deprecated) Spatial simultaneous autoregressive (SAR) structures

Set up COSY correlation structures

Category Specific Predictors in brms Models

Custom Families in brms Models

Prepare Predictor Data

Prepare Response Data

Compute Density Ratios

Extract Diagnostic Quantities of brms Models

Execute a Function Call

Support Functions for emmeans

Epileptic seizure counts

Expose user-defined Stan functions

Exponential function plus one.

Extract Model Family Objects

Fixed residual correlation (FCOR) structures

Expected Values of the Posterior Predictive Distribution

Extract Population-Level Estimates

Overview on Priors for brms Models

Get Reference Models

Extract response values

Set up Gaussian process terms in brms

Set up basic grouping terms in brms

Regularized horseshoe priors in brms

Non-Linear Hypothesis Testing

Clarity of inhaler instructions

Scaled inverse logit-link

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

K-Fold Cross-Validation

Predictions from K-Fold Cross-Validation

Infections in kidney patients

Set up a lasso prior in brms

Interface to shinystan

Compute the Pointwise Log-Likelihood

Scaled logit-link

Logarithm with a minus one offset.

Efficient approximate leave-one-out cross-validation (LOO)

Compute a LOO-adjusted R-squared for regression models

Model comparison with the loo package

Model averaging via stacking or pseudo-BMA weighting.

Moment matching for efficient approximate leave-one-out cross-validation

Compute Weighted Expectations Using LOO

Efficient approximate leave-one-out cross-validation (LOO) using subsampling

Cumulative Insurance Loss Payments

Set up MA(q) correlation structures

Prepare Fully Crossed Conditions

Stan Code for brms Models

Data for brms Models

MCMC Plots Implemented in bayesplot

Predictors with Measurement Error in brms Models

Predictors with Missing Values in brms Models

Finite Mixture Families in brms

Set up multi-membership grouping terms in brms

Multi-Membership Covariates

Monotonic Predictors in brms Models

Model Weighting Methods

Bind response variables in multivariate models

Set up a multivariate model formula for use in brms

Number of Grouping Factor Levels

Number of Posterior Samples

Create a matrix of output plots from a brmsfit object

Extract Parameter Names

Trace and Density Plots for MCMC Samples

Posterior Model Probabilities from Marginal Likelihoods

Posterior samples of parameters averaged across models

Expected Values of the Posterior Predictive Distribution

Compute posterior uncertainty intervals

Posterior Samples of the Linear Predictor

Samples from the Posterior Predictive Distribution

Extract Posterior Samples

Posterior Predictions of Smooth Terms

Summarize Posterior Samples

Table Creation for Posterior Samples

Posterior predictive samples averaged across models

Posterior Predictive Checks for brmsfit Objects

Posterior Probabilities of Mixture Component Memberships

Samples from the Posterior Predictive Distribution

Posterior Samples of Predictive Errors

Predictive Intervals

Prepare Predictions

Print a summary for a fitted model represented by a brmsfit object

Print method for brmsprior objects

Extract prior samples

Extract Priors of a Bayesian Model Fitted with brms

Extract Group-Level Estimates

Compute exact cross-validation for problematic observations

Rename Parameters

Posterior Samples of Residuals/Predictive Errors

Restructure Old brmsfit Objects

Convert Rows to Labels

Defining smooths in brms formulas

Spatial simultaneous autoregressive (SAR) structures

Control Saving of Parameter Draws

Prior Definitions for brms Models

Extract Stan model code

Extract data passed to Stan

User-defined variables passed to Stan

Create a summary of a fitted model represented by a brmsfit object

(Deprecated) Black Theme for ggplot2 Graphics

Default bayesplot Theme for ggplot2 Graphics

Threading in Stan

Update brms models

Update brms models based on multiple data sets

Update Formula Addition Terms

Validate New Data

Validate Prior for brms Models

Projection Predictive Variable Selection

Covariance and Correlation Matrix of Population-Level Effects

Widely Applicable Information Criterion (WAIC)