BDWreg
Bayesian Inference for Discrete Weibull Regression
A Bayesian regression model for discrete response, where the conditional distribution is modelled via a discrete Weibull distribution. This package provides an implementation of Metropolis-Hastings and Reversible-Jumps algorithms to draw samples from the posterior. It covers a wide range of regularizations through any two parameter prior. Examples are Laplace (Lasso), Gaussian (ridge), Uniform, Cauchy and customized priors like a mixture of priors. An extensive visual toolbox is included to check the validity of the results as well as several measures of goodness-of-fit.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3.0 |
rolling linux/jammy R-4.5 | BDWreg_1.3.0.tar.gz |
99.2 KiB |
1.3.0 |
rolling linux/noble R-4.5 | BDWreg_1.3.0.tar.gz |
99.0 KiB |
1.3.0 |
rolling source/ R- | BDWreg_1.3.0.tar.gz |
20.6 KiB |
1.3.0 |
latest linux/jammy R-4.5 | BDWreg_1.3.0.tar.gz |
99.2 KiB |
1.3.0 |
latest linux/noble R-4.5 | BDWreg_1.3.0.tar.gz |
99.0 KiB |
1.3.0 |
latest source/ R- | BDWreg_1.3.0.tar.gz |
20.6 KiB |
1.3.0 |
2026-04-26 source/ R- | BDWreg_1.3.0.tar.gz |
20.6 KiB |
1.3.0 |
2026-04-23 source/ R- | BDWreg_1.3.0.tar.gz |
20.6 KiB |
1.3.0 |
2026-04-09 windows/windows R-4.5 | BDWreg_1.3.0.zip |
101.6 KiB |
1.3.0 |
2025-04-20 source/ R- | BDWreg_1.3.0.tar.gz |
20.6 KiB |