HTLR
Bayesian Logistic Regression with Heavy-Tailed Priors
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), Journal of Statistical Computation and Simulation, 88:14, 2827-2851, <doi:10.48550/arXiv.1405.3319>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling source/ R- | HTLR_1.0.tar.gz |
1.5 MiB |
1.0 |
rolling linux/jammy R-4.5 | HTLR_1.0.tar.gz |
1.6 MiB |
1.0 |
rolling linux/noble R-4.5 | HTLR_1.0.tar.gz |
1.6 MiB |
1.0 |
latest source/ R- | HTLR_1.0.tar.gz |
1.5 MiB |
1.0 |
latest linux/jammy R-4.5 | HTLR_1.0.tar.gz |
1.6 MiB |
1.0 |
latest linux/noble R-4.5 | HTLR_1.0.tar.gz |
1.6 MiB |
1.0 |
2026-04-26 source/ R- | HTLR_1.0.tar.gz |
1.5 MiB |
1.0 |
2026-04-23 source/ R- | HTLR_1.0.tar.gz |
1.5 MiB |
1.0 |
2026-04-09 windows/windows R-4.5 | HTLR_1.0.zip |
2.0 MiB |
0.4-4 |
2025-04-20 source/ R- | HTLR_0.4-4.tar.gz |
1.7 MiB |