rstan-package {rstan} | R Documentation |
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 more information about Stan visit http://mc-stan.org/.
Package: | rstan |
Version: | 2.10.0 |
Date: | June 20, 2016 |
License: | GPL-3 |
For more information on Stan and its modeling language, see the Stan Modeling Language User's Guide and Reference Manual available at http://mc-stan.org/.
Authors: | Jiqiang Guo <guojq28@gmail.com> |
Ben Goodrich <benjamin.goodrich@columbia.edu> | |
Jonah Gabry >jsg2201@columbia.edu> | |
Maintainer: | Ben Goodrich <benjamin.goodrich@columbia.edu> |
Stan Development Team Stan Modeling Language User's Guide and Reference Manual. http://mc-stan.org/.
The stan
function for details on fitting models and
stanfit
for information on the fitted model objects.
Several related R packages are also available from the Stan Development Team: loo (loo-package) offers model comparison on estimated out-of-sample predictive performance, shinystan (shinystan-package) provides the ShinyStan GUI for exploring fitted Bayesian models, and rstanarm is an appendage to rstan providing an R formula interface for Bayesian regression modeling.
## 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) ## End(Not run)