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

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bpr.pdf |bpr.html
bpr/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

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

1.30 score 5 scripts 313 downloads 4 exports 6 dependencies

Last updated 7 months agofrom:84852e731b. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-win-x86_64OKOct 14 2024
R-4.5-linux-x86_64OKOct 14 2024
R-4.4-win-x86_64OKOct 14 2024
R-4.4-mac-x86_64OKOct 14 2024
R-4.4-mac-aarch64OKOct 14 2024
R-4.3-win-x86_64OKOct 14 2024
R-4.3-mac-x86_64OKOct 14 2024
R-4.3-mac-aarch64OKOct 14 2024

Exports:mcmc_diagnosticsmerge_simposterior_predictivesample_bpr

Dependencies:BHcodalatticeMASSRcppRcppArmadillo