Package: bpr 1.0.8
Laura DAngelo
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:
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.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://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:84852e731b. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 129 | ||
| linux-devel-x86_64 | OK | 153 | ||
| source / vignettes | OK | 250 | ||
| linux-release-arm64 | OK | 125 | ||
| linux-release-x86_64 | OK | 150 | ||
| windows-devel-arm64 | OK | 286 | ||
| windows-devel-x86_64 | OK | 217 | ||
| windows-release-arm64 | OK | 247 | ||
| windows-release-x86_64 | OK | 127 | ||
| wasm-release | OK | 140 |
Exports:mcmc_diagnosticsmerge_simposterior_predictivesample_bpr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| MCMC Convergence Diagnostics | mcmc_diagnostics |
| Merge Multiple Chains | merge_sim |
| Plot Trace and Distribution of Regression Parameters | plot.poisreg |
| Graphical Posterior Predictive Checks | plot.posterior_check |
| Compute Posterior Predictive Distribution | posterior_predictive |
| Fitting Bayesian Poisson Regression | sample_bpr |
| Summarizing Bayesian Poisson Regression Fit | print.poisreg summary.poisreg |