A B C D E F G H I L M N P R S T U Z
| 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 |
| breadReg | Bread for Sandwiches in Regularized Estimators |
| breadReg.zipath | Bread for Sandwiches in Regularized Estimators |
| breastfeed | Breast feeding decision |
| coef.cv.glmreg | Cross-validation for glmreg |
| coef.cv.irglmreg | Cross-validation for irglmreg |
| 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.irsvm | fit case weighted support vector machines with robust loss functions |
| coef.zipath | Methods for zipath Objects |
| compute_g | Compute concave function values |
| compute_wt | Weight value from concave function |
| 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.irglmreg | Cross-validation for irglmreg |
| cv.irglmreg.default | Cross-validation for irglmreg |
| cv.irglmreg.formula | Cross-validation for irglmreg |
| cv.irglmreg.matrix | Cross-validation for irglmreg |
| cv.irglmreg_fit | Internal function of cross-validation for irglmreg |
| cv.irsvm | Cross-validation for irsvm |
| cv.irsvm.default | Cross-validation for irsvm |
| cv.irsvm.formula | Cross-validation for irsvm |
| cv.irsvm.matrix | Cross-validation for irsvm |
| cv.irsvm_fit | Internal function of cross-validation for irsvm |
| 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 |
| docvisits | Doctor visits |
| 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 |
| gfunc | Convert response value to raw prediction in GLM |
| 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 |
| irglm | fit a robust generalized linear models |
| irglm.formula | fit a robust generalized linear models |
| irglmreg | Fit a robust penalized generalized linear models |
| irglmreg.default | Fit a robust penalized generalized linear models |
| irglmreg.formula | Fit a robust penalized generalized linear models |
| irglmreg.matrix | Fit a robust penalized generalized linear models |
| irglmreg_fit | Internal function for robust penalized generalized linear models |
| irsvm | fit case weighted support vector machines with robust loss functions |
| irsvm.default | fit case weighted support vector machines with robust loss functions |
| irsvm.formula | fit case weighted support vector machines with robust loss functions |
| irsvm.matrix | fit case weighted support vector machines with robust loss functions |
| irsvm_fit | Fit iteratively reweighted support vector machines for robust loss functions |
| logLik.glmreg | fit a GLM with lasso (or elastic net), snet or mnet regularization |
| logLik.zipath | Methods for zipath Objects |
| loss2 | Composite Loss Value |
| loss2_irsvm | Composite Loss Value for epsilon-insensitive Type |
| loss3 | Composite Loss Value for GLM |
| 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 | Optimize a nonconvex loss with regularization |
| nclreg.default | Optimize a nonconvex loss with regularization |
| nclreg.formula | Optimize a nonconvex loss with regularization |
| nclreg.matrix | Optimize a nonconvex loss with regularization |
| nclreg_fit | Internal function to fitting a nonconvex loss based robust linear model with regularization |
| ncl_fit | Internal function to fit a nonconvex loss based robust linear model |
| plot.cv.glmreg | Cross-validation for glmreg |
| plot.cv.irglmreg | Cross-validation for irglmreg |
| 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 |
| update_wt | Compute weight value |
| 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 |