SAMCpack: 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) <doi:10.1002/9780470669723> for complete introduction to the method.

Version: 0.1.1
Depends: R (≥ 3.0.0)
Imports: Rcpp, RcppXPtrUtils, utils, Rdpack
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, microbenchmark, pander, geoR, RandomFields
Published: 2018-10-26
Author: Yichen Cheng [aut], Ick Hoon Jin [aut], Faming Liang [aut], Kisung You [aut, cre]
Maintainer: Kisung You <kyou at nd.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
CRAN checks: SAMCpack results

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Reference manual: SAMCpack.pdf
Vignettes: Fast Computation with XPtr
Package source: SAMCpack_0.1.1.tar.gz
Windows binaries: r-devel: SAMCpack_0.1.1.zip, r-release: SAMCpack_0.1.1.zip, r-oldrel: SAMCpack_0.1.1.zip
OS X binaries: r-release: SAMCpack_0.1.1.tgz, r-oldrel: SAMCpack_0.1.1.tgz
Old sources: SAMCpack archive

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