Man pages for HTLR
Bayesian Logistic Regression with Heavy-Tailed Priors
| as.matrix.htlr.fit | Create a Matrix of Markov Chain Samples |
| bcbcsf_deltas | Bias-corrected Bayesian classification initial state |
| colon | Colon Tissues |
| diabetes392 | Pima Indians Diabetes |
| evaluate_pred | Evaluate Prediction Results |
| gendata_FAM | Generate Simulated Data with Factor Analysis Model |
| gendata_MLR | Generate Simulated Data with Multinomial Logistic Regression... |
| htlr | Fit a HTLR Model |
| htlr_fit | Fit a HTLR Model (Internal API) |
| HTLR-package | Bayesian Logistic Regression with Heavy-Tailed Priors |
| htlr_predict | Make Prediction on New Data (Advanced) |
| htlr_prior | Generate Prior Configuration |
| lasso_deltas | Lasso Initial State |
| nzero_idx | Get Indices of Non-Zero Coefficients |
| order_ftest | Order features by F-statistic |
| order_kruskal | Order features by Kruskal-Wallis test |
| order_plain | Plain order function |
| pipe | Pipe operator |
| predict.htlr.fit | Make Prediction on New Data |
| split_data | Split Data into Train and Test Partitions |
| std | Standardizes a Design Matrix |
| summary.htlr.fit | Posterior Summaries |
