Package: HTLR 0.4-4
Longhai Li
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, <arxiv:1405.3319>.
Authors:
HTLR_0.4-4.tar.gz
HTLR_0.4-4.tar.gz(r-4.5-noble)HTLR_0.4-4.tar.gz(r-4.4-noble)
HTLR_0.4-4.tgz(r-4.4-emscripten)HTLR_0.4-4.tgz(r-4.3-emscripten)
HTLR.pdf |HTLR.html✨
HTLR/json (API)
NEWS
# Install 'HTLR' in R: |
install.packages('HTLR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/longhaisk/htlr/issues
Pkgdown:https://longhaisk.github.io
- colon - Colon Tissues
- diabetes392 - Pima Indians Diabetes
Last updated 2 years agofrom:4aef542fd6. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 30 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 30 2024 |
Exports:%>%bcbcsf_deltasevaluate_predgendata_FAMgendata_MLRhtlrhtlr_fithtlr_predicthtlr_priorlasso_deltasnzero_idxorder_ftestorder_kruskalorder_plainsplit_datastd
Dependencies:abindBCBCSFcodetoolsforeachglmnetiteratorslatticemagrittrMatrixRcppRcppArmadilloRcppEigenshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Bayesian Logistic Regression with Heavy-Tailed Priors | HTLR-package |
Create a Matrix of Markov Chain Samples | as.matrix.htlr.fit |
Colon Tissues | colon |
Pima Indians Diabetes | diabetes392 |
Evaluate Prediction Results | evaluate_pred |
Generate Simulated Data with Factor Analysis Model | gendata_FAM |
Generate Simulated Data with Multinomial Logistic Regression Model | gendata_MLR |
Fit a HTLR Model | htlr |
Generate Prior Configuration | htlr_prior |
Get Indices of Non-Zero Coefficients | nzero_idx |
Make Prediction on New Data | predict.htlr.fit |
Split Data into Train and Test Partitions | split_data |
Standardizes a Design Matrix | std |
Posterior Summaries | summary.htlr.fit |