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https://github.com/stan-dev/rstanarm

rstanarm R package for Bayesian applied regression modeling
https://github.com/stan-dev/rstanarm

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models r r-package rstan rstanarm stan statistical-modeling

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rstanarm R package for Bayesian applied regression modeling

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README 

        
# rstanarm 



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### Bayesian applied regression modeling (arm) via Stan



This is an R package that emulates other R model-fitting functions but uses

[Stan](https://mc-stan.org) (via the **rstan** package) for the back-end

estimation. The primary target audience is people who would be open to Bayesian

inference if using Bayesian software were easier but would use frequentist

software otherwise. 



Fitting models with **rstanarm** is also useful for experienced Bayesian

software users who want to take advantage the pre-compiled Stan programs that

are written by Stan developers and carefully implemented to prioritize numerical

stability and the avoidance of sampling problems.



Click the arrows for more details:

More detail



The **rstanarm** package is an appendage to the **rstan** package, the R

interface to [Stan](https://mc-stan.org/). **rstanarm** enables many of the most

common applied regression models to be estimated using Markov Chain Monte Carlo,

variational approximations to the posterior distribution, or optimization. The

package allows these models to be specified using the customary R modeling

syntax (e.g., like that of `glm` with a `formula` and `data.frame`).

Additional arguments are provided for specifying prior distributions.



The set of models supported by **rstanarm** is large (and will continue to

grow), but also limited enough so that it is possible to integrate them

tightly with the [`pp_check`](https://mc-stan.org/rstanarm/reference/pp_check.stanreg.html) function for graphical posterior predictive checks using [**bayesplot**](https://mc-stan.org/bayesplot) and the

[`posterior_predict`](https://mc-stan.org/rstanarm/reference/posterior_predict.stanreg.html)

function to easily estimate the effect of specific manipulations of predictor

variables or to predict the outcome in a training set.



The fitted model objects returned by the **rstanarm** modeling functions are

called _stanreg_ objects. In addition to all of the traditional

[methods](https://mc-stan.org/rstanarm/reference/stanreg-methods.html)

defined for fitted model objects, stanreg objects can also be used with the

[**loo**](https://mc-stan.org/rstanarm/reference/loo.stanreg.html) package for

leave-one-out cross-validation, model comparison, and model weighting/averaging

and the [**shinystan**](https://mc-stan.org/rstanarm/reference/shinystan.html) 

package for exploring the posterior distribution and model diagnostics

with a graphical user interface. 



Check out the **rstanarm** [vignettes](https://mc-stan.org/rstanarm/articles/)

for examples and more details about the entire process.



Modeling functions



The model estimating functions are described in greater detail in their

individual help pages and vignettes. Here we provide a very brief overview:



* [__`stan_lm`__, __`stan_aov`__,__`stan_biglm`__](https://mc-stan.org/rstanarm/reference/stan_lm.html)



  Similar to  `lm` and `aov` but with novel regularizing priors on the model

  parameters that are driven by prior beliefs about R-squared, the proportion of

  variance in the outcome attributable to the predictors in a linear model.



* [__`stan_glm`__, __`stan_glm.nb`__](https://mc-stan.org/rstanarm/reference/stan_glm.html)



  Similar to `glm` but with various possible prior distributions for the

  coefficients and, if applicable, a prior distribution for any auxiliary

  parameter in a Generalized Linear Model (GLM) that is characterized by a

  `family` object (e.g. the shape parameter in Gamma models). It is also possible

  to estimate a negative binomial model similar to the `glm.nb` function

  in the `MASS` package.



* [__`stan_glmer`__, __`stan_glmer.nb`__, __`stan_lmer`__](https://mc-stan.org/rstanarm/reference/stan_glmer.html)



  Similar to the `glmer`, `glmer.nb`, and `lmer` functions (__lme4__ package) in

  that GLMs are augmented to have group-specific terms that deviate from the

  common coefficients according to a mean-zero multivariate normal distribution

  with a highly-structured but unknown covariance matrix (for which **rstanarm**

  introduces an innovative prior distribution). MCMC provides more appropriate

  estimates of uncertainty for models that consist of a mix of common and

  group-specific parameters.

  

* [__`stan_nlmer`__](https://mc-stan.org/rstanarm/reference/stan_nlmer.html)



  Similar to `nlmer` (__lme4__ package) package for nonlinear "mixed-effects"

  models, but flexible priors can be specified for all parameters in the model, 

  including the unknown covariance matrices for the varying 

  (group-specific) coefficients.



* [__`stan_gamm4`__](https://mc-stan.org/rstanarm/reference/stan_gamm4.html)



  Similar to `gamm4` (__gamm4__ package), which augments a GLM (possibly with

  group-specific terms) with nonlinear smooth functions of the predictors to

  form a Generalized Additive Mixed Model (GAMM). Rather than calling

  `lme4::glmer` like `gamm4` does, `stan_gamm4` essentially calls `stan_glmer`,

  which avoids the optimization issues that often crop up with GAMMs and

  provides better estimates for the uncertainty of the parameter estimates.

 

* [__`stan_polr`__](https://mc-stan.org/rstanarm/reference/stan_polr.html)



  Similar to `polr` (__MASS__ package) in that it models an ordinal response,

  but the Bayesian model also implies a prior distribution on the unknown

  cutpoints. Can also be used to model binary outcomes, possibly while

  estimating an unknown exponent governing the probability of success.

 

* [__`stan_betareg`__](https://mc-stan.org/rstanarm/reference/stan_betareg.html)



  Similar to `betareg` (__betareg__ package) in that it models an outcome that

  is a rate (proportion) but, rather than performing maximum likelihood

  estimation, full Bayesian estimation is performed by default, with

  customizable prior distributions for all parameters.



* [__`stan_clogit`__](https://mc-stan.org/rstanarm/reference/stan_clogit.html)



   Similar to `clogit` (__survival__ package) in that it models an binary outcome

   where the number of successes and failures is fixed within each stratum by

   the research design. There are some minor syntactical differences relative

   to `survival::clogit` that allow `stan_clogit` to accept

   group-specific terms as in `stan_glmer`.



* [__`stan_mvmer`__](https://mc-stan.org/rstanarm/reference/stan_mvmer.html)



   A multivariate form of `stan_glmer`, whereby the user can specify

   one or more submodels each consisting of a GLM with group-specific terms. If

   more than one submodel is specified (i.e. there is more than one outcome

   variable) then a dependence is induced by assuming that the group-specific

   terms for each grouping factor are correlated across submodels.



* [__`stan_jm`__](https://mc-stan.org/rstanarm/reference/stan_jm.html)



   Estimates shared parameter joint models for longitudinal and time-to-event

   (i.e. survival) data. The joint model can be univariate (i.e. one longitudinal

   outcome) or multivariate (i.e. more than one longitudinal outcome). A variety

   of parameterisations are available for linking the longitudinal and event

   processes (i.e. a variety of association structures).



Estimation algorithms



The modeling functions in the **rstanarm** package take an `algorithm`

argument that can be one of the following:



* __Sampling__ (`algorithm="sampling"`):

 

 Uses Markov Chain Monte Carlo (MCMC) --- in particular, Stan's implementation

 of Hamiltonian Monte Carlo (HMC) with a tuned but diagonal mass matrix --- 

 to draw from the posterior distribution of the parameters. This is the slowest

 but most reliable of the available estimation algorithms and it is __the

 default and recommended algorithm for statistical inference__.



* __Mean-field__ (`algorithm="meanfield"`):



 Uses mean-field variational inference to draw from an approximation to the

 posterior distribution. In particular, this algorithm finds the set of

 independent normal distributions in the unconstrained space that --- when

 transformed into the constrained space --- most closely approximate the

 posterior distribution. Then it draws repeatedly from these independent

 normal distributions and transforms them into the constrained space. The

 entire process is much faster than HMC and yields independent draws but

 __is not recommended for final statistical inference__. It can be useful to

 narrow the set of candidate models in large problems, particularly when

 specifying `QR=TRUE` in `stan_glm`, `stan_glmer`, and `stan_gamm4`, but is

 __only an approximation to the posterior distribution__.



* __Full-rank__ (`algorithm="fullrank"`):



 Uses full-rank variational inference to draw from an approximation to the

 posterior distribution by finding the multivariate normal distribution in

 the unconstrained space that --- when transformed into the constrained space

 --- most closely approximates the posterior distribution. Then it draws

 repeatedly from this multivariate normal distribution and transforms the

 draws into the constrained space. This process is slower than meanfield

 variational inference but is faster than HMC. Although still an

 approximation to the posterior distribution and thus __not recommended

 for final statistical inference__, the approximation is more realistic than

 that of mean-field variational inference because the parameters are not

 assumed to be independent in the unconstrained space. Nevertheless, fullrank

 variational inference is a more difficult optimization problem and the

 algorithm is more prone to non-convergence or convergence to a local

 optimum.



* __Optimizing__ (`algorithm="optimizing"`):



 Finds the posterior mode using a C++ implementation of the LBGFS algorithm. If

 there is no prior information, then this is equivalent to maximum likelihood,

 in which case there is no great reason to use the functions in the **rstanarm**

 package over the emulated functions in other packages. However, if priors are

 specified, then the estimates are penalized maximum likelihood estimates, which

 may have some redeeming value. Currently, optimization is only supported for

 `stan_glm`.



---



### Resources



* [mc-stan.org/rstanarm](https://mc-stan.org/rstanarm) (online documentation, vignettes)

* [Ask a question](https://discourse.mc-stan.org) (Stan Forums on Discourse)

* [Open an issue](https://github.com/stan-dev/rstanarm/issues) (GitHub issues for bug reports, feature requests)



### Installation



#### Latest Release



The most recent **rstanarm** release can be installed from CRAN via



```r

install.packages("rstanarm")

```



#### Development Version



To install from GitHub, first make sure that you can install the **rstan**

package and C++ toolchain by following these

[instructions](https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started).

Once **rstan** is successfully installed, you can install **rstanarm** from

GitHub using the **remotes** package by executing the following in R:



```r

# Change 2 to however many cores you can/want to use to parallelize install

# If you experience crashes or run out RAM during installation, try changing this to 1

Sys.setenv(MAKEFLAGS = "-j2")

Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = "true")

remotes::install_github("stan-dev/rstanarm", INSTALL_opts = "--no-multiarch", force = TRUE)

```



You can switch `build_vignettes` to `TRUE` but it takes a lot longer to install and the 

vignettes are already separately available from the 

[Stan website](https://mc-stan.org/rstanarm/articles/index.html) 

and 

[CRAN](https://cran.r-project.org/package=rstanarm/vignettes). 

If installation fails, please let us know by [filing an issue](https://github.com/stan-dev/rstanarm/issues).



#### Survival Analysis Version



The `feature/survival` branch on GitHub contains a development version of **rstanarm** that includes survival analysis functionality (via the `stan_surv` modelling function). Until this functionality is available in the CRAN release of **rstanarm**, users who wish to use the survival analysis functionality can install a binary version of the survival branch of **rstanarm** from the Stan R packages repository with:



```

install.packages("rstanarm", repos = c('https://stan-dev.r-universe.dev', getOption("repos")))

```



Note that this binary is static (i.e. it is not automatically updated) and is only hosted so that users can access the (experimental) survival analysis functionality without needing to go through the time consuming (and sometimes painful) task of installing the development version of **rstanarm** from source.



### Contributing 



If you are interested in contributing to the development of **rstanarm** please 

see the [developer notes](https://mc-stan.org/rstanarm/dev-notes/index.html) page.