Contemporaneous Markov Chain Monte Carlo

Implements contemporaneous Markov chain Monte Carlo (CMCMC) and interchain adaptive Markov chain Monte Carlo (INCA) samplers of Craiu, Rosenthal and Yang (2009) for targets known up to a normalising constant. The samplers run multiple Metropolis chains in parallel and update proposal covariance estimates using contemporaneous particle groups. Built-in target kernels include multivariate normal, logistic regression, Poisson, Gaussian, Gamma, and hierarchical models, with support for user-provided target kernels. The formula interface glm_cmcmc() fits supported generalized linear models using the built-in kernels. 'CUDA' is used when available, and an 'OpenMP'-enabled CPU backend is available on systems without a 'CUDA' compiler.

CMCMC

CMCMC implements contemporaneous Markov chain Monte Carlo and interchain adaptive MCMC (INCA) samplers for targets known up to a normalising constant. It includes built-in target kernels, a formula interface for supported GLMs, a CUDA backend when available, and an OpenMP-enabled CPU backend.

Installation

After the package is released on CRAN, install it with:

install.packages("CMCMC")

Then load it with:

library(CMCMC)