Package: bpr 1.0.8

bpr: Fitting Bayesian Poisson Regression

Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.

Authors:Laura D'Angelo

bpr_1.0.8.tar.gz
bpr_1.0.8.zip(r-4.7-x86_64)bpr_1.0.8.zip(r-4.7-arm64)bpr_1.0.8.zip(r-4.6-x86_64)bpr_1.0.8.zip(r-4.6-arm64)bpr_1.0.8.zip(r-4.5-x86_64)
bpr_1.0.8.tgz(r-4.6-x86_64)bpr_1.0.8.tgz(r-4.6-arm64)bpr_1.0.8.tgz(r-4.5-x86_64)bpr_1.0.8.tgz(r-4.5-arm64)
bpr_1.0.8.tar.gz(r-4.7-arm64)bpr_1.0.8.tar.gz(r-4.7-x86_64)bpr_1.0.8.tar.gz(r-4.6-arm64)bpr_1.0.8.tar.gz(r-4.6-x86_64)
bpr_1.0.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bpr/json (API)

# Install 'bpr' in R:
install.packages('bpr', repos = c('https://laura-dangelo.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 5 scripts 345 downloads 4 exports 6 dependencies

Last updated from:84852e731b. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK147
linux-devel-x86_64OK147
source / vignettesOK203
linux-release-arm64OK142
linux-release-x86_64OK161
macos-release-arm64OK174
macos-release-x86_64OK317
macos-oldrel-arm64OK182
macos-oldrel-x86_64OK207
windows-devel-arm64OK291
windows-devel-x86_64OK212
windows-release-arm64OK238
windows-release-x86_64OK190
windows-oldrel-x86_64OK194
wasm-releaseOK153

Exports:mcmc_diagnosticsmerge_simposterior_predictivesample_bpr

Dependencies:BHcodalatticeMASSRcppRcppArmadillo