R Interface to Stan
Create an mcmc.list from a stanfit object
Check HMC diagnostics after sampling
Expose user-defined Stan functions to for testing and simulation
Extract the compressed representation of a sparse matrix
Look up the Stan function that corresponds to a function or name.
Obtain the full path of file Makeconf
Compute summaries of MCMC draws and monitor convergence
Created named lists
RStan Plotting Functions
Print a summary for a fitted model represented by a stanfit object
Read data in an dump file to a list
Convergence and efficiency diagnostics for Markov Chains
Internal Functions and Methods
Create a Skeleton for a New Source Package with Stan Programs
RStan --- the interface to Stan
Set and read options used in RStan
Simulation Based Calibration (sbc)
Defunct function to set the compiler optimization level
Merge a list of stanfit objects into one
Fit a model with Stan
Read CSV files of samples generated by (R)Stan into a stanfit object
Demonstrate examples included in Stan
Construct a Stan model
ggplot2 for RStan
RStan Diagnostic plots
Set default appearance options
Dump the data for a Stan model to dump file in the limited format that...
Obtain the version of Stan
Translate Stan model specification to C++ code
Class stanfit: fitted Stan model
Extract samples from a fitted Stan model
log_prob and grad_log_prob functions
Moment matching for efficient approximate leave-one-out cross-validati...
Approximate leave-one-out cross-validation
Create a matrix of output plots from a stanfit object
Plots for stanfit objects
Summary method for stanfit objects
Markov chain traceplots
Create array, matrix, or data.frame objects from samples in a `stanfit...
Class representing model compiled from C++
Draw samples of generated quantities from a Stan model
Obtain a point estimate by maximizing the joint posterior
Draw samples from a Stan model
Run Stan's variational algorithm for approximate posterior sampling
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Useful links