# brms v2.14.0

Monthly downloads

## Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; B<c3><bc>rkner (2018) <doi:10.32614/RJ-2018-017>;
Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

## Readme

# brms

## Overview

The **brms** package provides an interface to fit Bayesian generalized
(non-)linear multivariate multilevel models using Stan, which is a C++
package for performing full Bayesian inference (see
https://mc-stan.org/).
The formula syntax is very similar to that of the package lme4 to
provide a familiar and simple interface for performing regression
analyses. A wide range of response distributions are supported, allowing
users to fit – among others – linear, robust linear, count data,
survival, response times, ordinal, zero-inflated, and even self-defined
mixture models all in a multilevel context. Further modeling options
include non-linear and smooth terms, auto-correlation structures,
censored data, missing value imputation, and quite a few more. In
addition, all parameters of the response distribution can be predicted
in order to perform distributional regression. Multivariate models
(i.e., models with multiple response variables) can be fit, as well.
Prior specifications are flexible and explicitly encourage users to
apply prior distributions that actually reflect their beliefs. Model fit
can easily be assessed and compared with posterior predictive checks,
cross-validation, and Bayes factors.

## Resources

- Introduction to brms (Journal of Statistical Software)
- Advanced multilevel modeling with brms (The R Journal)
- Website (Website of brms with documentation and vignettes)
- Blog posts (List of blog posts about brms)
- Ask a question (Stan Forums on Discourse)
- Open an issue (GitHub issues for bug reports and feature requests)

## How to use brms

```
library(brms)
```

As a simple example, we use poisson regression to model the seizure
counts in epileptic patients to investigate whether the treatment
(represented by variable `Trt`

) can reduce the seizure counts and
whether the effect of the treatment varies with the (standardized)
baseline number of seizures a person had before treatment (variable
`zBase`

). As we have multiple observations per person, a group-level
intercept is incorporated to account for the resulting dependency in the
data.

```
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = poisson())
```

The results (i.e., posterior samples) can be investigated using

```
summary(fit1)
#> Family: poisson
#> Links: mu = log
#> Formula: count ~ zAge + zBase * Trt + (1 | patient)
#> Data: epilepsy (Number of observations: 236)
#> Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#> total post-warmup samples = 4000
#>
#> Group-Level Effects:
#> ~patient (Number of levels: 59)
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept) 0.58 0.07 0.46 0.74 1.00 810 1753
#>
#> Population-Level Effects:
#> Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept 1.77 0.12 1.53 2.00 1.00 779 1319
#> zAge 0.09 0.09 -0.09 0.26 1.00 683 1071
#> zBase 0.70 0.12 0.46 0.95 1.00 847 1453
#> Trt1 -0.27 0.17 -0.59 0.06 1.00 661 1046
#> zBase:Trt1 0.05 0.16 -0.26 0.37 1.00 993 1624
#>
#> Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
```

On the top of the output, some general information on the model is given, such as family, formula, number of iterations and chains. Next, group-level effects are displayed seperately for each grouping factor in terms of standard deviations and (in case of more than one group-level effect per grouping factor; not displayed here) correlations between group-level effects. On the bottom of the output, population-level effects (i.e. regression coefficients) are displayed. If incorporated, autocorrelation effects and family specific parameters (e.g. the residual standard deviation ‘sigma’ in normal models) are also given.

In general, every parameter is summarized using the mean (‘Estimate’)
and the standard deviation (‘Est.Error’) of the posterior distribution
as well as two-sided 95% credible intervals (‘l-95% CI’ and ‘u-95% CI’)
based on quantiles. We see that the coefficient of `Trt`

is negative
with a zero overlapping 95%-CI. This indicates that, on average, the
treatment may reduce seizure counts by some amount but the evidence
based on the data and applied model is not very strong and still
insufficient by standard decision rules. Further, we find little
evidence that the treatment effect varies with the baseline number of
seizures.

The last two values (‘Eff.Sample’ and ‘Rhat’) provide information on how well the algorithm could estimate the posterior distribution of this parameter. If ‘Rhat’ is considerably greater than 1, the algorithm has not yet converged and it is necessary to run more iterations and / or set stronger priors.

To visually investigate the chains as well as the posterior
distributions, we can use the `plot`

method. If we just want to see
results of the regression coefficients of `Trt`

and `zBase`

, we go for

```
plot(fit1, pars = c("Trt", "zBase"))
```

A more detailed investigation can be performed by running
`launch_shinystan(fit1)`

. To better understand the relationship of the
predictors with the response, I recommend the `conditional_effects`

method:

```
plot(conditional_effects(fit1, effects = "zBase:Trt"))
```

This method uses some prediction functionality behind the scenes, which
can also be called directly. Suppose that we want to predict responses
(i.e. seizure counts) of a person in the treatment group (`Trt = 1`

) and
in the control group (`Trt = 0`

) with average age and average number of
previous seizures. Than we can use

```
newdata <- data.frame(Trt = c(0, 1), zAge = 0, zBase = 0)
predict(fit1, newdata = newdata, re_formula = NA)
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 5.8980 2.505627 2 11
#> [2,] 4.5595 2.162320 1 9
```

We need to set `re_formula = NA`

in order not to condition of the
group-level effects. While the `predict`

method returns predictions of
the responses, the `fitted`

method returns predictions of the regression
line.

```
fitted(fit1, newdata = newdata, re_formula = NA)
#> Estimate Est.Error Q2.5 Q97.5
#> [1,] 5.917144 0.7056695 4.632004 7.387471
#> [2,] 4.529949 0.5360204 3.544085 5.624005
```

Both methods return the same estimate (up to random error), while the
latter has smaller variance, because the uncertainty in the regression
line is smaller than the uncertainty in each response. If we want to
predict values of the original data, we can just leave the `newdata`

argument empty.

Suppose, we want to investigate whether there is overdispersion in the model, that is residual variation not accounted for by the response distribution. For this purpose, we include a second group-level intercept that captures possible overdispersion.

```
fit2 <- brm(count ~ zAge + zBase * Trt + (1|patient) + (1|obs),
data = epilepsy, family = poisson())
```

We can then go ahead and compare both models via approximate leave-one-out (LOO) cross-validation.

```
loo(fit1, fit2)
#> Output of model 'fit1':
#>
#> Computed from 4000 by 236 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -670.4 36.7
#> p_loo 92.8 14.3
#> looic 1340.8 73.3
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 214 90.7% 251
#> (0.5, 0.7] (ok) 17 7.2% 80
#> (0.7, 1] (bad) 3 1.3% 81
#> (1, Inf) (very bad) 2 0.8% 6
#> See help('pareto-k-diagnostic') for details.
#>
#> Output of model 'fit2':
#>
#> Computed from 4000 by 236 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -595.2 14.1
#> p_loo 108.0 7.3
#> looic 1190.4 28.2
#> ------
#> Monte Carlo SE of elpd_loo is NA.
#>
#> Pareto k diagnostic values:
#> Count Pct. Min. n_eff
#> (-Inf, 0.5] (good) 82 34.7% 544
#> (0.5, 0.7] (ok) 103 43.6% 153
#> (0.7, 1] (bad) 47 19.9% 22
#> (1, Inf) (very bad) 4 1.7% 7
#> See help('pareto-k-diagnostic') for details.
#>
#> Model comparisons:
#> elpd_diff se_diff
#> fit2 0.0 0.0
#> fit1 -75.2 26.9
```

The `loo`

output when comparing models is a little verbose. We first see
the individual LOO summaries of the two models and then the comparison
between them. Since higher `elpd`

(i.e., expected log posterior density)
values indicate better fit, we see that the model accounting for
overdispersion (i.e., `fit2`

) fits substantially better. However, we
also see in the individual LOO outputs that there are several
problematic observations for which the approximations may have not have
been very accurate. To deal with this appropriately, we need to fall
back to other methods such as `reloo`

or `kfold`

but this requires the
model to be refit several times which takes too long for the purpose of
a quick example. The post-processing methods we have shown above are
just the tip of the iceberg. For a full list of methods to apply on
fitted model objects, type `methods(class = "brmsfit")`

.

## Citing brms and related software

Developing and maintaining open source software is an important yet often underappreciated contribution to scientific progress. Thus, whenever you are using open source software (or software in general), please make sure to cite it appropriately so that developers get credit for their work.

When using brms, please cite one or more of the following publications:

- Bürkner P. C. (2017). brms: An R Package for Bayesian Multilevel
Models using Stan.
*Journal of Statistical Software*. 80(1), 1-28. doi.org/10.18637/jss.v080.i01 - Bürkner P. C. (2018). Advanced Bayesian Multilevel Modeling with the
R Package brms.
*The R Journal*. 10(1), 395-411. doi.org/10.32614/RJ-2018-017

As brms is a high-level interface to Stan, please additionally cite Stan:

- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,
Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).
Stan: A probabilistic programming language.
*Journal of Statistical Software*. 76(1). 10.18637/jss.v076.i01

Further, brms relies on several other R packages and, of course, on R
itself. To find out how to cite R and its packages, use the `citation`

function. There are some features of brms which specifically rely on
certain packages. The **rstan** package together with **Rcpp** makes
Stan conveniently accessible in R. Visualizations and
posterior-predictive checks are based on **bayesplot** and **ggplot2**.
Approximate leave-one-out cross-validation using `loo`

and related
methods is done via the **loo** package. Marginal likelihood based
methods such as `bayes_factor`

are realized by means of the
**bridgesampling** package. Splines specified via the `s`

and `t2`

functions rely on **mgcv**. If you use some of these features, please
also consider citing the related packages.

## FAQ

### How do I install brms?

To install the latest release version from CRAN use

```
install.packages("brms")
```

The current developmental version can be downloaded from github via

```
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("paul-buerkner/brms")
```

Because brms is based on Stan, a C++ compiler is required. The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On Mac, you should install Xcode. For further instructions on how to get the compilers running, see the prerequisites section on https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started.

### I am new to brms. Where can I start?

Detailed instructions and case studies are given in the package’s
extensive vignettes. See `vignette(package = "brms")`

for an overview.
For documentation on formula syntax, families, and prior distributions
see `help("brm")`

.

### Where do I ask questions, propose a new feature, or report a bug?

Questions can be asked on the Stan forums on Discourse. To propose a new feature or report a bug, please open an issue on GitHub.

### How can I extract the generated Stan code?

If you have already fitted a model, just apply the `stancode`

method on
the fitted model object. If you just want to generate the Stan code
without any model fitting, use the `make_stancode`

function.

### Can I avoid compiling models?

When you fit your model for the first time with brms, there is currently
no way to avoid compilation. However, if you have already fitted your
model and want to run it again, for instance with more samples, you can
do this without recompilation by using the `update`

method. For more
details see `help("update.brmsfit")`

.

### What is the difference between brms and rstanarm?

The rstanarm package is similar to brms in that it also allows to fit
regression models using Stan for the backend estimation. Contrary to
brms, rstanarm comes with precompiled code to save the compilation time
(and the need for a C++ compiler) when fitting a model. However, as brms
generates its Stan code on the fly, it offers much more flexibility in
model specification than rstanarm. Also, multilevel models are currently
fitted a bit more efficiently in brms. For detailed comparisons of brms
with other common R packages implementing multilevel models, see
`vignette("brms_multilevel")`

and `vignette("brms_overview")`

.

## Functions in brms

Name | Description | |

Shifted_Lognormal | The Shifted Log Normal Distribution | |

GenExtremeValue | The Generalized Extreme Value Distribution | |

Dirichlet | The Dirichlet Distribution | |

ExGaussian | The Exponentially Modified Gaussian Distribution | |

Frechet | The Frechet Distribution | |

AsymLaplace | The Asymmetric Laplace Distribution | |

InvGaussian | The Inverse Gaussian Distribution | |

MultiStudentT | The Multivariate Student-t Distribution | |

MultiNormal | The Multivariate Normal Distribution | |

Wiener | The Wiener Diffusion Model Distribution | |

StudentT | The Student-t Distribution | |

SkewNormal | The Skew-Normal Distribution | |

ZeroInflated | Zero-Inflated Distributions | |

ar | Set up AR(p) correlation structures | |

autocor.brmsfit | (Deprecated) Extract Autocorrelation Objects | |

autocor-terms | Autocorrelation structures | |

as.mcmc.brmsfit | Extract posterior samples for use with the coda package | |

arma | Set up ARMA(p,q) correlation structures | |

brm | Fit Bayesian Generalized (Non-)Linear Multivariate Multilevel Models | |

bridge_sampler.brmsfit | Log Marginal Likelihood via Bridge Sampling | |

brms-package | Bayesian Regression Models using 'Stan' | |

brm_multiple | Run the same brms model on multiple datasets | |

VarCorr.brmsfit | Extract Variance and Correlation Components | |

add_criterion | Add model fit criteria to model objects | |

add_loo | Add model fit criteria to model objects | |

VonMises | The von Mises Distribution | |

bayes_R2.brmsfit | Compute a Bayesian version of R-squared for regression models | |

bayes_factor.brmsfit | Bayes Factors from Marginal Likelihoods | |

Hurdle | Hurdle Distributions | |

addition-terms | Additional Response Information | |

brmshypothesis | Descriptions of brmshypothesis Objects | |

compare_ic | Compare Information Criteria of Different Models | |

combine_models | Combine Models fitted with brms | |

brmsfamily | Special Family Functions for brms Models | |

brmsfit-class | Class brmsfit of models fitted with the brms package | |

brmsformula-helpers | Linear and Non-linear formulas in brms | |

brmsformula | Set up a model formula for use in brms | |

brmsterms | Parse Formulas of brms Models | |

cor_car | (Deprecated) Spatial conditional autoregressive (CAR) structures | |

cor_cosy | (Deprecated) Compound Symmetry (COSY) Correlation Structure | |

car | Spatial conditional autoregressive (CAR) structures | |

coef.brmsfit | Extract Model Coefficients | |

cor_ar | (Deprecated) AR(p) correlation structure | |

cor_brms | (Deprecated) Correlation structure classes for the brms package | |

control_params | Extract Control Parameters of the NUTS Sampler | |

cor_bsts | (Defunct) Basic Bayesian Structural Time Series | |

cor_arma | (Deprecated) ARMA(p,q) correlation structure | |

cor_arr | (Defunct) ARR correlation structure | |

conditional_effects.brmsfit | Display Conditional Effects of Predictors | |

cosy | Set up COSY correlation structures | |

cor_sar | (Deprecated) Spatial simultaneous autoregressive (SAR) structures | |

expose_functions.brmsfit | Expose user-defined Stan functions | |

cor_ma | (Deprecated) MA(q) correlation structure | |

conditional_smooths.brmsfit | Display Smooth Terms | |

density_ratio | Compute Density Ratios | |

cor_fixed | (Deprecated) Fixed user-defined covariance matrices | |

epilepsy | Epileptic seizure counts | |

fixef.brmsfit | Extract Population-Level Estimates | |

diagnostic-quantities | Extract Diagnostic Quantities of brms Models | |

cs | Category Specific Predictors in brms Models | |

data_predictor | Prepare Predictor Data | |

hypothesis.brmsfit | Non-Linear Hypothesis Testing | |

is.brmsprior | Checks if argument is a brmsprior object | |

get_prior | Overview on Priors for brms Models | |

is.brmsterms | Checks if argument is a brmsterms object | |

expp1 | Exponential function plus one. | |

get_y | Extract response values | |

gp | Set up Gaussian process terms in brms | |

is.cor_brms | Check if argument is a correlation structure | |

is.mvbrmsformula | Checks if argument is a mvbrmsformula object | |

family.brmsfit | Extract Model Family Objects | |

data_response | Prepare Response Data | |

logm1 | Logarithm with a minus one offset. | |

is.brmsfit_multiple | Checks if argument is a brmsfit_multiple object | |

is.brmsformula | Checks if argument is a brmsformula object | |

kfold_predict | Predictions from K-Fold Cross-Validation | |

lasso | Set up a lasso prior in brms | |

inhaler | Clarity of inhaler instructions | |

fcor | Fixed residual correlation (FCOR) structures | |

fitted.brmsfit | Expected Values of the Posterior Predictive Distribution | |

inv_logit_scaled | Scaled inverse logit-link | |

launch_shinystan.brmsfit | Interface to shinystan | |

custom_family | Custom Families in brms Models | |

do_call | Execute a Function Call | |

loo.brmsfit | Efficient approximate leave-one-out cross-validation (LOO) | |

emmeans-brms-helpers | Support Functions for emmeans | |

is.brmsfit | Checks if argument is a brmsfit object | |

is.mvbrmsterms | Checks if argument is a mvbrmsterms object | |

mixture | Finite Mixture Families in brms | |

mm | Set up multi-membership grouping terms in brms | |

loo_subsample.brmsfit | Efficient approximate leave-one-out cross-validation (LOO) using subsampling | |

loo_predict.brmsfit | Compute Weighted Expectations Using LOO | |

gr | Set up basic grouping terms in brms | |

kfold.brmsfit | K-Fold Cross-Validation | |

loo_model_weights.brmsfit | Model averaging via stacking or pseudo-BMA weighting. | |

make_conditions | Prepare Fully Crossed Conditions | |

make_stancode | Stan Code for brms Models | |

loo_moment_match.brmsfit | Moment matching for efficient approximate leave-one-out cross-validation | |

me | Predictors with Measurement Error in brms Models | |

kidney | Infections in kidney patients | |

loo_R2.brmsfit | Compute a LOO-adjusted R-squared for regression models | |

mvbind | Bind response variables in multivariate models | |

model_weights.brmsfit | Model Weighting Methods | |

posterior_epred.brmsfit | Expected Values of the Posterior Predictive Distribution | |

mmc | Multi-Membership Covariates | |

mi | Predictors with Missing Values in brms Models | |

horseshoe | Regularized horseshoe priors in brms | |

mo | Monotonic Predictors in brms Models | |

posterior_interval.brmsfit | Compute posterior uncertainty intervals | |

rows2labels | Convert Rows to Labels | |

ngrps.brmsfit | Number of Grouping Factor Levels | |

mvbrmsformula | Set up a multivariate model formula for use in brms | |

s | Defining smooths in brms formulas | |

posterior_smooths.brmsfit | Posterior Predictions of Smooth Terms | |

posterior_samples.brmsfit | Extract Posterior Samples | |

log_lik.brmsfit | Compute the Pointwise Log-Likelihood | |

pp_check.brmsfit | Posterior Predictive Checks for brmsfit Objects | |

logit_scaled | Scaled logit-link | |

pp_average.brmsfit | Posterior predictive samples averaged across models | |

posterior_summary | Summarize Posterior Samples | |

make_standata | Data for brms Models | |

loo_compare.brmsfit | Model comparison with the loo package | |

mcmc_plot.brmsfit | MCMC Plots Implemented in bayesplot | |

nsamples.brmsfit | Number of Posterior Samples | |

update.brmsfit | Update brms models | |

update.brmsfit_multiple | Update brms models based on multiple data sets | |

posterior_linpred.brmsfit | Posterior Samples of the Linear Predictor | |

restructure | Restructure Old brmsfit Objects | |

prior_samples.brmsfit | Extract prior samples | |

pairs.brmsfit | Create a matrix of output plots from a brmsfit object | |

residuals.brmsfit | Posterior Samples of Residuals/Predictive Errors | |

posterior_predict.brmsfit | Samples from the Posterior Predictive Distribution | |

print.brmsprior | Print method for brmsprior objects | |

theme_default | Default bayesplot Theme for ggplot2 Graphics | |

loss | Cumulative Insurance Loss Payments | |

pp_mixture.brmsfit | Posterior Probabilities of Mixture Component Memberships | |

ma | Set up MA(q) correlation structures | |

threading | Threading in Stan | |

prepare_predictions.brmsfit | Prepare Predictions | |

posterior_table | Table Creation for Posterior Samples | |

parnames | Extract Parameter Names | |

predict.brmsfit | Samples from the Posterior Predictive Distribution | |

standata.brmsfit | Extract data passed to Stan | |

stanvar | User-defined variables passed to Stan | |

vcov.brmsfit | Covariance and Correlation Matrix of Population-Level Effects | |

waic.brmsfit | Widely Applicable Information Criterion (WAIC) | |

plot.brmsfit | Trace and Density Plots for MCMC Samples | |

print.brmsfit | Print a summary for a fitted model represented by a brmsfit object | |

rename_pars | Rename Parameters | |

stancode.brmsfit | Extract Stan model code | |

reloo.brmsfit | Compute exact cross-validation for problematic observations | |

set_prior | Prior Definitions for brms Models | |

update_adterms | Update Formula Addition Terms | |

sar | Spatial simultaneous autoregressive (SAR) structures | |

save_pars | Control Saving of Parameter Draws | |

post_prob.brmsfit | Posterior Model Probabilities from Marginal Likelihoods | |

validate_newdata | Validate New Data | |

posterior_average.brmsfit | Posterior samples of parameters averaged across models | |

predictive_error.brmsfit | Posterior Samples of Predictive Errors | |

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

theme_black | (Deprecated) Black Theme for ggplot2 Graphics | |

ranef.brmsfit | Extract Group-Level Estimates | |

predictive_interval.brmsfit | Predictive Intervals | |

prior_summary.brmsfit | Extract Priors of a Bayesian Model Fitted with brms | |

No Results! |

## Vignettes of brms

## Last month downloads

## Details

Encoding | UTF-8 |

Type | Package |

Date | 2020-10-07 |

LazyData | true |

NeedsCompilation | no |

License | GPL-2 |

URL | https://github.com/paul-buerkner/brms, https://discourse.mc-stan.org/ |

BugReports | https://github.com/paul-buerkner/brms/issues |

Additional_repositories | https://mc-stan.org/r-packages/ |

VignetteBuilder | knitr, R.rsp |

RoxygenNote | 7.1.1 |

Packaged | 2020-10-08 12:53:46 UTC; paulb |

Repository | CRAN |

Date/Publication | 2020-10-08 14:20:03 UTC |

imports | abind , backports , bayesplot (>= 1.5.0) , bridgesampling (>= 0.3-0) , coda , future , ggplot2 (>= 2.0.0) , glue (>= 1.3.0) , grDevices , loo (>= 2.3.1) , Matrix (>= 1.1.1) , matrixStats , mgcv (>= 1.8-13) , nleqslv , nlme , parallel , rstan (>= 2.19.2) , rstantools (>= 2.1.1) , shinystan (>= 2.4.0) , stats , utils |

suggests | ape , arm , cmdstanr (>= 0.1.3) , digest , emmeans (>= 1.4.2) , knitr , lme4 , MCMCglmm , mice , mnormt , R.rsp , rmarkdown , rtdists , RWiener , spdep , splines2 , statmod , testthat (>= 0.9.1) |

depends | methods , R (>= 3.5.0) , Rcpp (>= 0.12.0) |

Contributors | Jonah Gabry, Sebastian Weber |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/brms)](http://www.rdocumentation.org/packages/brms)
```