As.mcmc.list

object of class <code>&quot;stanfit&quot;</code>

object

optional character vector of parameters to include

pars

logical scalar indicating whether to include (the default) or 
    exclude theh parameters named in <code>pars</code>

include

&#8230;

Create an <code><a rd-options="coda" href="/link/mcmc.list?package=rstan&version=2.15.1&to=coda" data-mini-rdoc="coda::mcmc.list">mcmc.list</a></code> (<span class="pkg">coda</span>) 
  from a <code>stanfit</code> object.

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

As.mcmc.list function

Create an <code><a rd-options='coda' href='mcmc.list'>mcmc.list</a></code> (<span class="pkg">coda</span>) 
  from a <code>stanfit</code> object.

Create an mcmc.list from a stanfit object — As.mcmc.list

Create an mcmc.list from a stanfit object

As.mcmc.list: Create an mcmc.list from a stanfit object

Description

Usage

Arguments

Value