Package: reglogit 1.2-7
reglogit: Simulation-Based Regularized Logistic Regression
Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).
Authors:
reglogit_1.2-7.tar.gz
reglogit_1.2-7.zip(r-4.5)reglogit_1.2-7.zip(r-4.4)reglogit_1.2-7.zip(r-4.3)
reglogit_1.2-7.tgz(r-4.4-x86_64)reglogit_1.2-7.tgz(r-4.4-arm64)reglogit_1.2-7.tgz(r-4.3-x86_64)reglogit_1.2-7.tgz(r-4.3-arm64)
reglogit_1.2-7.tar.gz(r-4.5-noble)reglogit_1.2-7.tar.gz(r-4.4-noble)
reglogit_1.2-7.tgz(r-4.4-emscripten)reglogit_1.2-7.tgz(r-4.3-emscripten)
reglogit.pdf |reglogit.html✨
reglogit/json (API)
| # Install 'reglogit' in R: |
| install.packages('reglogit', repos = c('https://rbgramacy.r-universe.dev', 'https://cloud.r-project.org')) |
- pima - Pima Indian Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:2d19b31353. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 21 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 21 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 21 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 21 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 21 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 21 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 21 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 21 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 21 2024 |
Exports:beta.dRUMcalc.Cscalc.lpostcalc.mlpostdraw.betadraw.lambdadraw.nudraw.omegadraw.zgibbs.dRUMmpreprocessmy.rinvgausspredict.reglogitpredict.regmlogitpreprocessreglogitregmlogitrmultnormz.dRUM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Pima Indian Data | pima |
| Prediction for regularized (polychotomous) logistic regression models | predict.reglogit predict.regmlogit |
| Gibbs sampling for regularized logistic regression | reglogit regmlogit |
