rstan (version 2.17.3)

rstan-package: RStan --- R interface to Stan

Description

Stan Development Team

RStan is the R interface to the Stan C++ package. RStan 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 the Stan modeling language see the Stan Modeling Language User's Guide and Reference Manual.

Other R packages from the Stan Development Team

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

Package	Description	Link
bayesplot	ggplot-based plotting library for graphing parameter estimates, MCMC diagnostics, and posterior predictive checks.	bayesplot-package
shinystan	Interactive visual summaries and advanced posterior analysis of MCMC output.	shinystan-package
loo	Out-of-sample predictive performance estimates and model comparison.	loo-package
rstanarm	R formula interface for Bayesian applied regression modeling.	rstanarm-package
rstantools	Tools for developers of R packages interfacing with Stan.	rstantools-package

See Also

• stan for details on fitting models and '>stanfit for information on the fitted model objects.

• 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-users forum.

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, sample_file = 'norm.csv',
            verbose = TRUE) 
print(fit)
traceplot(fit)

# extract samples 
e <- extract(fit, permuted = TRUE) # return a list of arrays 
mu <- e$mu 

m <- extract(fit, permuted = FALSE, inc_warmup = FALSE) # return an array 
print(dimnames(m))

# using as.array directly on stanfit objects 
m2 <- as.array(fit)

# }