rjpdmp: Reversible Jump PDMP Samplers

Provides an implementation of the reversible jump piecewise deterministic Markov processes (PDMPs) methods developed in the paper Reversible Jump PDMP Samplers for Variable Selection (Chevallier, Fearnhead, Sutton 2020, <arXiv:2010.11771>). It also contains an implementation of a Gibbs sampler for variable selection in Logistic regression based on Polya-Gamma augmentation.

Version:	2.0.0
Imports:	data.table, Rcpp (≥ 0.12.3)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	MASS
Published:	2022-02-21
Author:	Matt Sutton, Augustin Chevalier, Paul Fearnhead, with PolyaGamma simulation code contributed from Jesse Windle and James G. Scott (<https://github.com/jgscott/helloPG>)
Maintainer:	Matt Sutton <matt.sutton.stat at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	yes
Materials:	README
CRAN checks:	rjpdmp results

Documentation:

Reference manual:

rjpdmp.pdf

Downloads:

Package source:	rjpdmp_2.0.0.tar.gz
Windows binaries:	r-devel: rjpdmp_2.0.0.zip, r-release: rjpdmp_2.0.0.zip, r-oldrel: rjpdmp_2.0.0.zip
macOS binaries:	r-release (arm64): rjpdmp_2.0.0.tgz, r-oldrel (arm64): rjpdmp_2.0.0.tgz, r-release (x86_64): rjpdmp_2.0.0.tgz, r-oldrel (x86_64): rjpdmp_2.0.0.tgz
Old sources:	rjpdmp archive

Linking:

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