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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.
Getting started
Browse package contents
Package details |
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| 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@gmail.com> |
| License | GPL (>= 2) |
| Version | 2.0.0 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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