adaptMCMC-package {adaptMCMC}R Documentation

Generic adaptive Monte Carlo Markov Chain sampler


Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.


Package: adaptMCMC
Type: Package
Version: 1.4
Date: 2021-03-29
License: GPL (>= 2)
LazyLoad: yes

The workhorse function is MCMC. Chains can be updated with MCMC.add.samples. MCMC.parallel is a wrapper to generate independent chains on several CPU's in parallel using parallel. coda-functions can be used after conversion with


Andreas Scheidegger, or


Vihola, M. (2012) Robust adaptive Metropolis algorithm with coerced acceptance rate. Statistics and Computing, 22(5), 997-1008. doi:10.1007/s11222-011-9269-5.

See Also

MCMC, MCMC.add.samples, MCMC.parallel,

The package HI provides an adaptive rejection Metropolis sampler with the function arms. See also Metro_Hastings of the MHadaptive package.

[Package adaptMCMC version 1.4 Index]