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
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HTLR_0.4-4.tgz(r-4.4-x86_64)HTLR_0.4-4.tgz(r-4.4-arm64)HTLR_0.4-4.tgz(r-4.3-x86_64)HTLR_0.4-4.tgz(r-4.3-arm64)
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HTLR.pdf |HTLR.html✨
HTLR/json (API)
NEWS
| # Install 'HTLR' in R: |
| install.packages('HTLR', repos = c('https://longhaisk.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/longhaisk/htlr/issues
- colon - Colon Tissues
- diabetes392 - Pima Indians Diabetes
bayesianclassificationhigh-dimensional-datamachine-learningmcmc
Last updated 9 days agofrom:af90142e1f. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 14 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 14 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 14 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 14 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 14 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 14 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 14 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 14 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 14 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 |
|---|---|
| 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 |