Functions for Monte Carlo Methods with R

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Documentation for package ‘mcsm’ version 1.0

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adapump Illustration of the danger of adaptive MCMC for the pump failure data
betagen Plot explaining accept-reject on a Beta(2.7,6.3) target
Braking Quadratic regression on the car braking dataset
challenge Slice sampler analysis of the challenger dataset
challenger O-ring failures against temperature for shuttle launches
dmunorm Density function of the multivariate normal distribution
dyadic A dyadic antithetic improvement for a toy problem
EMcenso EM paths for a censored normal model
Energy Energy intake
gibbsmix Implementation of a Gibbs sampler on a mixture posterior
hastings Reproduction of Hastings' experiment
jamestein Monte Carlo plots of the risks of James-Stein estimators
kscheck Convergence assessment for the pump failure data
logima Logistic analysis of the Pima.tr dataset with control variates
maximple Graphical representation of a toy example of simulated annealing
mhmix Implement two Metropolis-Hastings algorithms on a mixture posterior
mochoice An MCMC model choice illustration for the linear model
mump Illustration of Gelman and Rubin's diagnostic on the pump failure data
normbyde Compare two double-exponentials approximations to a normal distribution
pimamh Langevin MCMC algorithm for the probit posterior
pimax Monte Carlo approximation of a probit posterior marginal
randogibs First illustrations of coda's output for the one-way random effect model
randogit MCEM resolution for a probit maximum likelihood
randomeff Gibbs sampler for a one-way random effect model
rbeta Plot explaining accept-reject on a Beta(2.7,6.3) target
rchisq Poor chi-square generator
rdirichlet Dirichlet generator
reparareff Reparameterized version of the one-way random effect model
rmunorm Random generator for the multivariate normal distribution
SAmix Graphical representation of the simulated annealing sequence for the mixture posterior
sqar Illustration of some of coda's criterions on the noisy squared AR model
sqaradap Illustration of the dangers of doing adaptive MCMC on a noisy squared AR model
test1 Poor chi-square generator
test2 Generic chi-square generator
test3 Approximate Poisson generator
test4 Replicate Poisson generator