| AIC.glmreg | Methods for mpath Objects |
| AIC.zipath | Methods for mpath Objects |
| be.zeroinfl | conduct backward stepwise variable elimination for zero inflated count regression |
| BIC.glmreg | Methods for mpath Objects |
| BIC.zipath | Methods for mpath Objects |
| bioChemists | article production by graduate students in biochemistry Ph.D. programs |
| breadReg | Bread for Sandwiches in Regularized Estimators |
| breadReg.zipath | Bread for Sandwiches in Regularized Estimators |
| coef.cv.glmreg | Cross-validation for glmreg |
| coef.cv.nclreg | Cross-validation for nclreg |
| coef.cv.zipath | Cross-validation for zipath |
| coef.glmreg | Model predictions based on a fitted "glmreg" object. |
| coef.zipath | Methods for zipath Objects |
| conv2glmreg | convert glm object to class glmreg |
| conv2zipath | convert zeroinfl object to class zipath |
| cv.glmreg | Cross-validation for glmreg |
| cv.glmreg.default | Cross-validation for glmreg |
| cv.glmreg.formula | Cross-validation for glmreg |
| cv.glmreg.matrix | Cross-validation for glmreg |
| cv.glmregNB | Cross-validation for glmregNB |
| cv.glmreg_fit | Internal function of cross-validation for glmreg |
| cv.nclreg | Cross-validation for nclreg |
| cv.nclreg.default | Cross-validation for nclreg |
| cv.nclreg.formula | Cross-validation for nclreg |
| cv.nclreg.matrix | Cross-validation for nclreg |
| cv.nclreg_fit | Internal function of cross-validation for nclreg |
| cv.zipath | Cross-validation for zipath |
| cv.zipath.default | Cross-validation for zipath |
| cv.zipath.formula | Cross-validation for zipath |
| cv.zipath.matrix | Cross-validation for zipath |
| cv.zipath_fit | Cross-validation for zipath |
| deviance.glmreg | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| estfunReg | Extract Empirical First Derivative of Log-likelihood Function |
| estfunReg.zipath | Extract Empirical First Derivative of Log-likelihood Function |
| fitted.zipath | Methods for zipath Objects |
| glmreg | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| glmreg.default | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| glmreg.formula | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| glmreg.matrix | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| glmregNB | fit a negative binomial model with lasso (or elastic net), snet and mnet regularization |
| glmregNegbin | fit a negative binomial model with lasso (or elastic net), snet and mnet regularization |
| glmreg_fit | Internal function to fit a GLM with lasso (or elastic net), snet and mnet regularization |
| hessianReg | Hessian Matrix of Regularized Estimators |
| logLik.glmreg | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| logLik.zipath | Methods for zipath Objects |
| meatReg | Meat Matrix Estimator |
| model.matrix.zipath | Methods for zipath Objects |
| ncl | fit a nonconvex loss based robust linear model |
| ncl.default | fit a nonconvex loss based robust linear model |
| ncl.formula | fit a nonconvex loss based robust linear model |
| ncl.matrix | fit a nonconvex loss based robust linear model |
| nclreg | fit a nonconvex loss based robust linear model with lasso (or elastic net), snet or mnet regularization |
| nclreg.default | fit a nonconvex loss based robust linear model with lasso (or elastic net), snet or mnet regularization |
| nclreg.formula | fit a nonconvex loss based robust linear model with lasso (or elastic net), snet or mnet regularization |
| nclreg.matrix | fit a nonconvex loss based robust linear model with lasso (or elastic net), snet or mnet regularization |
| nclreg_fit | Internal function to fit a nonconvex loss based robust linear model with lasso (or elastic net), snet and mnet regularization |
| ncl_fit | Internal function to fit a nonconvex loss based robust linear model |
| plot.cv.glmreg | Cross-validation for glmreg |
| plot.cv.nclreg | Cross-validation for nclreg |
| plot.glmreg | plot coefficients from a "glmreg" object |
| predict.cv.glmreg | Cross-validation for glmreg |
| predict.cv.zipath | Cross-validation for zipath |
| predict.glmreg | Model predictions based on a fitted "glmreg" object. |
| predict.zipath | Methods for zipath Objects |
| predprob.zipath | Methods for zipath Objects |
| print.summary.glmregNB | Summary Method Function for Objects of Class 'glmregNB' |
| print.summary.zipath | Methods for zipath Objects |
| pval.zipath | compute p-values from penalized zero-inflated model with multi-split data |
| residuals.zipath | Methods for zipath Objects |
| rzi | random number generation of zero-inflated count response |
| sandwichReg | Making Sandwiches with Bread and Meat for Regularized Estimators |
| se | Standard Error of Regularized Estimators |
| se.zipath | Standard Error of Regularized Estimators |
| stan | standardize variables |
| summary.glmregNB | Summary Method Function for Objects of Class 'glmregNB' |
| summary.zipath | Methods for zipath Objects |
| terms.zipath | Methods for zipath Objects |
| tuning.zipath | find optimal path for penalized zero-inflated model |
| zipath | Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |
| zipath.default | Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |
| zipath.formula | Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |
| zipath.matrix | Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |
| zipath_fit | Internal function to fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |