rstan (version 2.19.2)

rstan-package: 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 <span style="R">R</span> packages from the Stan Development Team

Various related R packages are also available from the Stan Development Team:

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

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)
# }