General Markov Chain Monte Carlo for Bayesian Inference using adaptive Metropolis-Hastings sampling

Performs general Metropolis-Hastings Markov Chain Monte Carlo sampling of a user defined function which returns the un-normalized value (likelihood times prior) of a Bayesian model. The proposal variance-covariance structure is updated adaptively for efficient mixing when the structure of the target distribution is unknown. The package also provides some functions for Bayesian inference including Bayesian Credible Intervals (BCI) and Deviance Information Criterion (DIC) calculation.


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install.packages("MHadaptive")

1.1-8 by Corey Chivers, 7 years ago


Browse source code at https://github.com/cran/MHadaptive


Authors: Corey Chivers


Documentation:   PDF Manual  


GPL (>= 3) license


Depends on MASS


Imported by pssm, support.

Depended on by AdjBQR, ltbayes.


See at CRAN