R: RStan - the R interface to Stan

rstan-package {rstan} | R Documentation

## RStan — the R interface to Stan

### Description

Stan Development Team

RStan is the R interface to the Stan C++ package. The RStan interface (rstan R package) provides:

- Full Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo (HMC)

- Approximate Bayesian inference using automatic differentiation variational inference (ADVI)

- Penalized maximum likelihood estimation using L-BFGS optimization

For documentation on Stan itself, including the manual and user guide for the modeling language, case studies and worked examples, and other tutorial information visit the Users section of the Stan website:

- mc-stan.org/users/documentation

### Other R packages from the Stan Development Team

Various related R packages are also available from the Stan Development Team including these and more:

Package | Description | Doc | Website
bayesplot | ggplot-based plotting of parameter estimates, diagnostics, and posterior predictive checks. | bayesplot-package | mc-stan.org/bayesplot
shinystan | Interactive GUI for exploring MCMC output. | shinystan-package | mc-stan.org/shinystan
loo | Out-of-sample predictive performance estimates and model comparison. | loo-package | mc-stan.org/loo
rstanarm | R formula interface for applied regression modeling. | rstanarm-package | mc-stan.org/rstanarm
rstantools | Tools for developers of R packages interfacing with Stan. | rstantools-package | mc-stan.org/rstantools

### Author(s)

Jonah Gabry (author) | <jonah.sol.gabry@columbia.edu>
Ben Goodrich (maintainer, author) | <benjamin.goodrich@columbia.edu>
Jiqiang Guo (author) | <guojq28@gmail.com>

There are also many other important contributors to RStan (github.com/rstan). Please use 'Stan Development Team' whenever citing the R interface to Stan. A BibTex entry is available from http://mc-stan.org/rstan/authors or `citation("rstan")`.

### See Also

- The RStan vignettes: http://mc-stan.org/rstan/articles/.

- `stan` for details on fitting models and `stanfit` for information on the fitted model objects.

- The `lookup` for finding a function in the Stan language that corresponds to a R function or name.

- https://github.com/stan-dev/rstan/issues/ to submit a bug report or feature request.

- http://discourse.mc-stan.org to ask a question on the Stan Forums.

### Examples

    ## Not run:

    stanmodelcode <- "
    data {
      int<lower=0> N;
      real y[N];
    }

    parameters {
      real mu;
    }

    model {
      target += normal_lpdf(mu | 0, 10);
      target += normal_lpdf(y  | mu, 1);
    }
    "

    y <- rnorm(20)
    dat <- list(N = 20, y = y);
    fit <- stan(model_code = stanmodelcode, model_name = "example",
                data = dat, iter = 2012, chains = 3, verbose = TRUE,
                sample_file = file.path(tempdir(), 'norm.csv'))
    print(fit)

    # extract samples
    e <- extract(fit, permuted = FALSE) # return a list of arrays
    str(e)

    arr <- as.array(fit) # return an array
    str(arr)

    mat <- as.matrix(fit) # return a matrix
    str(mat)

    ## End(Not run)

[Package rstan version 2.21.2 Index]