All functions

dasym_laplace() pasym_laplace() qasym_laplace() rasym_laplace()

The Asymmetric Laplace Distribution

dbeta_binomial() pbeta_binomial() rbeta_binomial()

The Beta-binomial Distribution

ddirichlet() rdirichlet()

The Dirichlet Distribution

dexgaussian() pexgaussian() rexgaussian()

The Exponentially Modified Gaussian Distribution

dfrechet() pfrechet() qfrechet() rfrechet()

The Frechet Distribution

dgen_extreme_value() pgen_extreme_value() qgen_extreme_value() rgen_extreme_value()

The Generalized Extreme Value Distribution

dhurdle_poisson() phurdle_poisson() dhurdle_negbinomial() phurdle_negbinomial() dhurdle_gamma() phurdle_gamma() dhurdle_lognormal() phurdle_lognormal()

Hurdle Distributions

dinv_gaussian() pinv_gaussian() rinv_gaussian()

The Inverse Gaussian Distribution

dlogistic_normal() rlogistic_normal()

The (Multivariate) Logistic Normal Distribution

dmulti_normal() rmulti_normal()

The Multivariate Normal Distribution

dmulti_student_t() rmulti_student_t()

The Multivariate Student-t Distribution

R2D2()

R2D2 Priors in brms

dshifted_lnorm() pshifted_lnorm() qshifted_lnorm() rshifted_lnorm()

The Shifted Log Normal Distribution

dskew_normal() pskew_normal() qskew_normal() rskew_normal()

The Skew-Normal Distribution

dstudent_t() pstudent_t() qstudent_t() rstudent_t()

The Student-t Distribution

VarCorr(<brmsfit>)

Extract Variance and Correlation Components

dvon_mises() pvon_mises() rvon_mises()

The von Mises Distribution

dwiener() rwiener()

The Wiener Diffusion Model Distribution

dzero_inflated_poisson() pzero_inflated_poisson() dzero_inflated_negbinomial() pzero_inflated_negbinomial() dzero_inflated_binomial() pzero_inflated_binomial() dzero_inflated_beta_binomial() pzero_inflated_beta_binomial() dzero_inflated_beta() pzero_inflated_beta()

Zero-Inflated Distributions

add_criterion()

Add model fit criteria to model objects

add_loo() add_waic() add_ic() add_ic<-()

Add model fit criteria to model objects

add_rstan_model()

Add compiled rstan models to brmsfit objects

resp_se() resp_weights() resp_trials() resp_thres() resp_cat() resp_dec() resp_cens() resp_trunc() resp_mi() resp_index() resp_rate() resp_subset() resp_vreal() resp_vint()

ar()

Set up AR(p) correlation structures

arma()

Set up ARMA(p,q) correlation structures

as.brmsprior()

Transform into a brmsprior object

as.data.frame(<brmsfit>) as.matrix(<brmsfit>) as.array(<brmsfit>)

Extract Posterior Draws

as.mcmc(<brmsfit>)

Extract posterior samples for use with the coda package

autocor-terms

Autocorrelation structures

autocor()

(Deprecated) Extract Autocorrelation Objects

bayes_R2(<brmsfit>)

Compute a Bayesian version of R-squared for regression models

bayes_factor(<brmsfit>)

Bayes Factors from Marginal Likelihoods

bridge_sampler(<brmsfit>)

Log Marginal Likelihood via Bridge Sampling

brm()

Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models

brm_multiple()

Run the same brms model on multiple datasets

brms-package brms

Bayesian Regression Models using 'Stan'

brmsfamily() student() bernoulli() beta_binomial() negbinomial() geometric() lognormal() shifted_lognormal() skew_normal() exponential() weibull() frechet() gen_extreme_value() exgaussian() wiener() Beta() dirichlet() logistic_normal() von_mises() asym_laplace() cox() hurdle_poisson() hurdle_negbinomial() hurdle_gamma() hurdle_lognormal() hurdle_cumulative() zero_inflated_beta() zero_one_inflated_beta() zero_inflated_poisson() zero_inflated_negbinomial() zero_inflated_binomial() zero_inflated_beta_binomial() categorical() multinomial() cumulative() sratio() cratio() acat()

Special Family Functions for brms Models

brmsfit-class brmsfit

Class brmsfit of models fitted with the brms package

nlf() lf() acformula() set_nl() set_rescor() set_mecor()

Linear and Non-linear formulas in brms

brmsformula()

Set up a model formula for use in brms

print(<brmshypothesis>) plot(<brmshypothesis>)

Descriptions of brmshypothesis Objects

brmsterms()

Parse Formulas of brms Models

car()

Spatial conditional autoregressive (CAR) structures

coef(<brmsfit>)

Extract Model Coefficients

combine_models()

Combine Models fitted with brms

compare_ic()

Compare Information Criteria of Different Models

conditional_effects() plot(<brms_conditional_effects>)

Display Conditional Effects of Predictors

conditional_smooths()

Display Smooth Terms

control_params()

Extract Control Parameters of the NUTS Sampler

cor_ar()

(Deprecated) AR(p) correlation structure

cor_arma()

(Deprecated) ARMA(p,q) correlation structure

cor_brms cor_brms-class

(Deprecated) Correlation structure classes for the brms package

cor_car() cor_icar()

(Deprecated) Spatial conditional autoregressive (CAR) structures

cor_cosy()

(Deprecated) Compound Symmetry (COSY) Correlation Structure

cor_fixed()

(Deprecated) Fixed user-defined covariance matrices

cor_ma()

(Deprecated) MA(q) correlation structure

cor_sar() cor_lagsar() cor_errorsar()

(Deprecated) Spatial simultaneous autoregressive (SAR) structures

cosy()

Set up COSY correlation structures

cs()

Category Specific Predictors in brms Models

custom_family()

Custom Families in brms Models

density_ratio()

Compute Density Ratios

log_posterior(<brmsfit>) nuts_params(<brmsfit>) rhat(<brmsfit>) neff_ratio(<brmsfit>)

Extract Diagnostic Quantities of brms Models

as_draws(<brmsfit>) as_draws_matrix(<brmsfit>) as_draws_array(<brmsfit>) as_draws_df(<brmsfit>) as_draws_list(<brmsfit>) as_draws_rvars(<brmsfit>)

Transform brmsfit to draws objects

variables(<brmsfit>) nvariables(<brmsfit>) niterations(<brmsfit>) nchains(<brmsfit>) ndraws(<brmsfit>)

Index brmsfit objects

recover_data.brmsfit() emm_basis.brmsfit()

Support Functions for emmeans

epilepsy

Epileptic seizure counts

expose_functions()

Expose user-defined Stan functions

expp1()

Exponential function plus one.

family(<brmsfit>)

Extract Model Family Objects

fcor()

Fixed residual correlation (FCOR) structures

fitted(<brmsfit>)

Expected Values of the Posterior Predictive Distribution

fixef(<brmsfit>)

Extract Population-Level Estimates

get_dpar()

Draws of a Distributional Parameter

get_prior()

Overview on Priors for brms Models

get_refmodel.brmsfit()

Projection Predictive Variable Selection: Get Reference Model

gp()

Set up Gaussian process terms in brms

gr()

Set up basic grouping terms in brms

horseshoe()

Regularized horseshoe priors in brms

hypothesis()

Non-Linear Hypothesis Testing

inhaler

Clarity of inhaler instructions

inv_logit_scaled()

is.brmsfit()

Checks if argument is a brmsfit object

is.brmsfit_multiple()

Checks if argument is a brmsfit_multiple object

is.brmsformula()

Checks if argument is a brmsformula object

is.brmsprior()

Checks if argument is a brmsprior object

is.brmsterms()

Checks if argument is a brmsterms object

is.cor_brms() is.cor_arma() is.cor_cosy() is.cor_sar() is.cor_car() is.cor_fixed()

Check if argument is a correlation structure

is.mvbrmsformula()

Checks if argument is a mvbrmsformula object

is.mvbrmsterms()

Checks if argument is a mvbrmsterms object

kfold(<brmsfit>)

K-Fold Cross-Validation

kfold_predict()

Predictions from K-Fold Cross-Validation

kidney

Infections in kidney patients

lasso()

(Defunct) Set up a lasso prior in brms

launch_shinystan(<brmsfit>)

Interface to shinystan

log_lik(<brmsfit>)

Compute the Pointwise Log-Likelihood

logit_scaled()

logm1()

Logarithm with a minus one offset.

loo(<brmsfit>)

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

loo_R2(<brmsfit>)

Compute a LOO-adjusted R-squared for regression models

loo_compare(<brmsfit>)

Model comparison with the loo package

loo_model_weights(<brmsfit>)

Model averaging via stacking or pseudo-BMA weighting.

loo_moment_match(<brmsfit>)

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

loo_predict(<brmsfit>) loo_linpred(<brmsfit>) loo_predictive_interval(<brmsfit>)

Compute Weighted Expectations Using LOO

loo_subsample(<brmsfit>)

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

loss

Cumulative Insurance Loss Payments

ma()

Set up MA(q) correlation structures

make_conditions()

Prepare Fully Crossed Conditions

make_stancode()

Stan Code for brms Models

make_standata()

Data for brms Models

mcmc_plot()

MCMC Plots Implemented in bayesplot

me()

Predictors with Measurement Error in brms Models

mi()

Predictors with Missing Values in brms Models

mixture()

Finite Mixture Families in brms

mm()

Set up multi-membership grouping terms in brms

mmc()

Multi-Membership Covariates

mo()

Monotonic Predictors in brms Models

model_weights()

Model Weighting Methods

mvbind()

Bind response variables in multivariate models

mvbrmsformula()

Set up a multivariate model formula for use in brms

ngrps()

Number of Grouping Factor Levels

nsamples(<brmsfit>)

(Deprecated) Number of Posterior Samples

opencl()

GPU support in Stan via OpenCL

pairs(<brmsfit>)

Create a matrix of output plots from a brmsfit object

parnames()

Extract Parameter Names

plot(<brmsfit>)

Trace and Density Plots for MCMC Draws

post_prob(<brmsfit>)

Posterior Model Probabilities from Marginal Likelihoods

posterior_average()

Posterior draws of parameters averaged across models

posterior_epred(<brmsfit>)

Draws from the Expected Value of the Posterior Predictive Distribution

posterior_interval(<brmsfit>)

Compute posterior uncertainty intervals

posterior_linpred(<brmsfit>)

Posterior Draws of the Linear Predictor

posterior_predict(<brmsfit>)

Draws from the Posterior Predictive Distribution

posterior_samples()

(Deprecated) Extract Posterior Samples

posterior_smooths()

Posterior Predictions of Smooth Terms

posterior_summary()

Summarize Posterior draws

posterior_table()

Table Creation for Posterior Draws

pp_average()

Posterior predictive draws averaged across models

pp_check(<brmsfit>)

Posterior Predictive Checks for brmsfit Objects

pp_mixture()

Posterior Probabilities of Mixture Component Memberships

predict(<brmsfit>)

Draws from the Posterior Predictive Distribution

predictive_error(<brmsfit>)

Posterior Draws of Predictive Errors

predictive_interval(<brmsfit>)

Predictive Intervals

prepare_predictions()

Prepare Predictions

print(<brmsfit>)

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

print(<brmsprior>)

Print method for brmsprior objects

prior_draws() prior_samples()

Extract Prior Draws

prior_summary(<brmsfit>)

Extract Priors of a Bayesian Model Fitted with brms

psis(<brmsfit>)

Pareto smoothed importance sampling (PSIS)

ranef(<brmsfit>)

Extract Group-Level Estimates

recompile_model()

Recompile Stan models in brmsfit objects

reloo()

Compute exact cross-validation for problematic observations

rename_pars()

Rename parameters in brmsfit objects

residuals(<brmsfit>)

Posterior Draws of Residuals/Predictive Errors

restructure()

Restructure Old brmsfit Objects

rows2labels()

Convert Rows to Labels

s() t2()

Defining smooths in brms formulas

sar()

Spatial simultaneous autoregressive (SAR) structures

save_pars()

Control Saving of Parameter Draws

set_prior() prior() prior_() prior_string() empty_prior()

Prior Definitions for brms Models

stancode()

Extract Stan model code

standata()

Extract data passed to Stan

stanvar()

User-defined variables passed to Stan

summary(<brmsfit>)

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

theme_black()

(Deprecated) Black Theme for ggplot2 Graphics

theme_default

Default bayesplot Theme for ggplot2 Graphics

threading()

unstr()

Set up UNSTR correlation structures

update(<brmsfit>)

Update brms models

update(<brmsfit_multiple>)

Update brms models based on multiple data sets

update_adterms()

Update Formula Addition Terms

validate_newdata()

Validate New Data

validate_prior()

Validate Prior for brms Models

vcov(<brmsfit>)

Covariance and Correlation Matrix of Population-Level Effects

waic(<brmsfit>)

Widely Applicable Information Criterion (WAIC)