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: | 0.1.0 |
| Imports: | data.table, Rcpp (≥ 0.12.3) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | MASS |
| Published: | 2020-12-10 |
| 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 |
| Reference manual: | rjpdmp.pdf |
| Package source: | rjpdmp_0.1.0.tar.gz |
| Windows binaries: | r-devel: rjpdmp_0.1.0.zip, r-release: rjpdmp_0.1.0.zip, r-oldrel: rjpdmp_0.1.0.zip |
| macOS binaries: | r-release: rjpdmp_0.1.0.tgz, r-oldrel: rjpdmp_0.1.0.tgz |
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