AsymLaplace             The Asymmetric Laplace Distribution
BetaBinomial            The Beta-binomial Distribution
Dirichlet               The Dirichlet Distribution
ExGaussian              The Exponentially Modified Gaussian
                        Distribution
Frechet                 The Frechet Distribution
GenExtremeValue         The Generalized Extreme Value Distribution
Hurdle                  Hurdle Distributions
InvGaussian             The Inverse Gaussian Distribution
LogisticNormal          The (Multivariate) Logistic Normal Distribution
MultiNormal             The Multivariate Normal Distribution
MultiStudentT           The Multivariate Student-t Distribution
R2D2                    R2-D2 Priors in 'brms'
Shifted_Lognormal       The Shifted Log Normal Distribution
SkewNormal              The Skew-Normal Distribution
StudentT                The Student-t Distribution
VarCorr.brmsfit         Extract Variance and Correlation Components
VonMises                The von Mises Distribution
Wiener                  The Wiener Diffusion Model Distribution
ZeroInflated            Zero-Inflated Distributions
add_criterion           Add model fit criteria to model objects
add_loo                 Add model fit criteria to model objects
add_rstan_model         Add compiled 'rstan' models to 'brmsfit'
                        objects
addition-terms          Additional Response Information
ar                      Set up AR(p) correlation structures
arma                    Set up ARMA(p,q) correlation structures
as.data.frame.brmsfit   Extract Posterior Draws
as.mcmc.brmsfit         Extract posterior samples for use with the
                        'coda' package
autocor-terms           Autocorrelation structures
autocor.brmsfit         (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            Bayesian Regression Models using 'Stan'
brmsfamily              Special Family Functions for 'brms' Models
brmsfit-class           Class 'brmsfit' of models fitted with the
                        'brms' package
brmsformula             Set up a model formula for use in 'brms'
brmsformula-helpers     Linear and Non-linear formulas in 'brms'
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.brmsfit
                        Display Conditional Effects of Predictors
conditional_smooths.brmsfit
                        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                (Deprecated) Correlation structure classes for
                        the 'brms' package
cor_car                 (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                 (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
diagnostic-quantities   Extract Diagnostic Quantities of 'brms' Models
draws-brms              Transform 'brmsfit' to 'draws' objects
draws-index-brms        Index 'brmsfit' objects
emmeans-brms-helpers    Support Functions for 'emmeans'
epilepsy                Epileptic seizure counts
expose_functions.brmsfit
                        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.brmsfit      Non-Linear Hypothesis Testing
inhaler                 Clarity of inhaler instructions
inv_logit_scaled        Scaled inverse logit-link
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             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                   Set up a lasso prior in 'brms'
launch_shinystan.brmsfit
                        Interface to 'shinystan'
log_lik.brmsfit         Compute the Pointwise Log-Likelihood
logit_scaled            Scaled logit-link
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     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.brmsfit       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.brmsfit   Model Weighting Methods
mvbind                  Bind response variables in multivariate models
mvbrmsformula           Set up a multivariate model formula for use in
                        'brms'
ngrps.brmsfit           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.brmsfit
                        Posterior draws of parameters averaged across
                        models
posterior_epred.brmsfit
                        Expected Values 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.brmsfit
                        (Deprecated) Extract Posterior Samples
posterior_smooths.brmsfit
                        Posterior Predictions of Smooth Terms
posterior_summary       Summarize Posterior draws
posterior_table         Table Creation for Posterior Draws
pp_average.brmsfit      Posterior predictive draws averaged across
                        models
pp_check.brmsfit        Posterior Predictive Checks for 'brmsfit'
                        Objects
pp_mixture.brmsfit      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.brmsfit
                        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.brmsfit     Extract Prior Draws
prior_summary.brmsfit   Extract Priors of a Bayesian Model Fitted with
                        'brms'
ranef.brmsfit           Extract Group-Level Estimates
recompile_model         Recompile Stan models in 'brmsfit' objects
reloo.brmsfit           Compute exact cross-validation for problematic
                        observations
rename_pars             Rename Parameters
residuals.brmsfit       Posterior Draws of Residuals/Predictive Errors
restructure             Restructure Old 'brmsfit' Objects
rows2labels             Convert Rows to Labels
s                       Defining smooths in 'brms' formulas
sar                     Spatial simultaneous autoregressive (SAR)
                        structures
save_pars               Control Saving of Parameter Draws
set_prior               Prior Definitions for 'brms' Models
stancode.brmsfit        Extract Stan model code
standata.brmsfit        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               Threading in Stan
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)
