rstan-package {rstan} | R Documentation |
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.
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 |
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
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.
https://groups.google.com/forum/#!forum/stan-users/ to ask a question on the Stan-users forum.
## 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)