rstan-internal

get_cppcode,ANY-method

get_logposterior,ANY-method

get_adaptation_info,ANY-method

get_sampler_params,ANY-method

get_posterior_mean,ANY-method

get_elapsed_time,ANY-method

get_num_upars,ANY-method

log_prob,ANY-method

grad_log_prob,ANY-method

unconstrain_pars,ANY-method

constrain_pars,ANY-method

cpp_object_initializer

cxxdso-class

show,cxxdso-method

grab_cxxfun,cxxdso-method

is_dso_loaded,cxxdso-method

<p>These functions are not intended to be called directly. In the case of
methods, they are documented elsewhere, either with the associated generic
function or with the class of the object for which the method is defined.</p>

internal

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.

Ben Goodrich

rstan

R Interface to Stan

Jiqiang Guo

Jonah Gabry

Daniel Lee

Krzysztof Sakrejda

Trustees of Columbia University

Oleg Sklyar

The R Core Team

Jens Oehlschlaegel-Akiyoshi

Hadley Wickham

Joel de Guzman

John Fletcher

Thomas Heller

Eric Niebler

rstan-internal function

Internal Functions and Methods — rstan-internal

Internal Functions and Methods

rstan-internal: Internal Functions and Methods

Description

Arguments