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 |
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 |
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 |
print.zipath |
Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization |
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 |