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mcsm (version 1.0)

Functions for Monte Carlo Methods with R

Description

mcsm contains a collection of functions that allows the reenactment of the R programs used in the book EnteR Monte Carlo Methods without further programming. Programs being available as well, they can be modified by the user to conduct one's own simulations.

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Version

Install

install.packages('mcsm')

Monthly Downloads

20

Version

1.0

License

GPL (>= 2)

Maintainer

Christian P Robert

Last Published

April 28th, 2009

Functions in mcsm (1.0)

EM paths for a censored normal model

Graphical representation of the simulated annealing sequence for the mixture posterior

Monte Carlo plots of the risks of James-Stein estimators

Convergence assessment for the pump failure data

Reproduction of Hastings' experiment

Graphical representation of a toy example of simulated annealing

Implementation of a Gibbs sampler on a mixture posterior

Replicate Poisson generator

Illustration of Gelman and Rubin's diagnostic on the pump failure data

Slice sampler analysis of the challenger dataset

Logistic analysis of the Pima.tr dataset with control variates

Poor chi-square generator

Generic chi-square generator

O-ring failures against temperature for shuttle launches

Reparameterized version of the one-way random effect model

Plot explaining accept-reject on a Beta(2.7,6.3) target

A dyadic antithetic improvement for a toy problem

Illustration of some of coda's criterions on the noisy squared AR model

Implement two Metropolis-Hastings algorithms on a mixture posterior

Quadratic regression on the car braking dataset

Illustration of the dangers of doing adaptive MCMC on a noisy squared AR model

Random generator for the multivariate normal distribution

An MCMC model choice illustration for the linear model

Illustration of the danger of adaptive MCMC for the pump failure data

Density function of the multivariate normal distribution

Monte Carlo approximation of a probit posterior marginal

Langevin MCMC algorithm for the probit posterior

Gibbs sampler for a one-way random effect model

Compare two double-exponentials approximations to a normal distribution

Approximate Poisson generator

Dirichlet generator

First illustrations of coda's output for the one-way random effect model

MCEM resolution for a probit maximum likelihood