Stochastic Approximation Monte Carlo (SAMC) Sampler and Methods

Stochastic Approximation Monte Carlo (SAMC) is one of the celebrated Markov chain Monte Carlo (MCMC) algorithms. It is known to be capable of sampling from multimodal or doubly intractable distributions. We provide generic SAMC samplers for continuous distributions. User-specified densities in R and C++ are both supported. We also provide functions for specific problems that exploit SAMC computation. See Liang et al (2010) for complete introduction to the method.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.1.1 by Kisung You, a year ago

Browse source code at

Authors: Yichen Cheng [aut] , Ick Hoon Jin [aut] , Faming Liang [aut] , Kisung You [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Rcpp, RcppXPtrUtils, utils, Rdpack

Suggests knitr, rmarkdown, microbenchmark, pander, geoR, RandomFields

Linking to Rcpp, RcppArmadillo

See at CRAN