Package: pexm 1.1.3

Vinicius Mayrink

pexm: Loading a JAGS Module for the Piecewise Exponential Distribution

Load the Just Another Gibbs Sampling (JAGS) module 'pexm'. The module provides the tools to work with the Piecewise Exponential (PE) distribution in a Bayesian model with the corresponding Markov Chain Monte Carlo algorithm (Gibbs Sampling) implemented via JAGS. Details about the module implementation can be found in Mayrink et al. (2021) <doi:10.18637/jss.v100.i08>.

Authors:Vinicius Mayrink [aut, cre], Joao Duarte [aut], Fabio Demarqui [ctb]

pexm_1.1.3.tar.gz
pexm_1.1.3.zip(r-4.7-x86_64)pexm_1.1.3.zip(r-4.7-arm64)pexm_1.1.3.zip(r-4.6-x86_64)pexm_1.1.3.zip(r-4.6-arm64)pexm_1.1.3.zip(r-4.5-x86_64)
pexm_1.1.3.tgz(r-4.6-x86_64)pexm_1.1.3.tgz(r-4.6-arm64)pexm_1.1.3.tgz(r-4.5-x86_64)pexm_1.1.3.tgz(r-4.5-arm64)
pexm_1.1.3.tar.gz(r-4.7-arm64)pexm_1.1.3.tar.gz(r-4.7-x86_64)pexm_1.1.3.tar.gz(r-4.6-arm64)pexm_1.1.3.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pexm/json (API)

# Install 'pexm' in R:
install.packages('pexm', repos = c('https://vdinizm.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vdinizm/pexm/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

3.30 score 4 stars 2 scripts 277 downloads 1 exports 19 dependencies

Last updated from:978a57ff99. Checks:13 NOTE, 1 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE117
linux-devel-x86_64NOTE136
source / vignettesOK167
linux-release-arm64NOTE133
linux-release-x86_64NOTE120
macos-release-arm64NOTE163
macos-release-x86_64NOTE259
macos-oldrel-arm64NOTE155
macos-oldrel-x86_64NOTE499
windows-devel-arm64NOTE368
windows-devel-x86_64NOTE110
windows-release-arm64NOTE380
windows-release-x86_64NOTE102
windows-oldrel-x86_64NOTE118
wasm-releaseFAIL86

Exports:loadpexm

Dependencies:clicodaexpmgenericsgluelatticelifecyclemagrittrMatrixmsmmvtnormpillarpkgconfigrjagsrlangsurvivaltibbleutf8vctrs