Regularized Linear Models
Meat Matrix Estimator
Convert response value to raw prediction in GLM
Internal function to fit a GLM with lasso (or elastic net), snet and m...
conduct backward stepwise variable elimination for zero inflated count...
Bread for Sandwiches in Regularized Estimators
Compute concave function values
Weight value from concave function
convert glm object to class glmreg
convert zeroinfl object to class zipath
Internal function of cross-validation for glmreg
Cross-validation for glmreg
Cross-validation for glmregNB
Internal function of cross-validation for irglmreg
fit a GLM with lasso (or elastic net), snet or mnet regularization
fit a negative binomial model with lasso (or elastic net), snet and mn...
Cross-validation for irglmreg
Internal function of cross-validation for irsvm
Cross-validation for irsvm
Internal function of cross-validation for nclreg
Cross-validation for nclreg
Cross-validation for zipath
Cross-validation for zipath
Extract Empirical First Derivative of Log-likelihood Function
Hessian Matrix of Regularized Estimators
fit a robust generalized linear models
Internal function for robust penalized generalized linear models
Fit a robust penalized generalized linear models
Fit iteratively reweighted support vector machines for robust loss fun...
fit case weighted support vector machines with robust loss functions
Composite Loss Value for epsilon-insensitive Type
Composite Loss Value
Composite Loss Value for GLM
Methods for mpath Objects
Internal mpath functions
Internal function to fit a nonconvex loss based robust linear model
fit a nonconvex loss based robust linear model
Internal function to fitting a nonconvex loss based robust linear mode...
Optimize a nonconvex loss with regularization
compute p-values from penalized zero-inflated model with multi-split d...
plot coefficients from a "glmreg" object
Model predictions based on a fitted "glmreg" object.
Methods for zipath Objects
random number generation of zero-inflated count response
Making Sandwiches with Bread and Meat for Regularized Estimators
Standard Error of Regularized Estimators
standardize variables
Summary Method Function for Objects of Class 'glmregNB'
find optimal path for penalized zero-inflated model
Compute weight value
Internal function to fit zero-inflated count data linear model with la...
Fit zero-inflated count data linear model with lasso (or elastic net),...
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2024) <doi:10.1111/anzs.12409>.