mpath0.4-2.26 package

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>.

Maintainer: Zhu Wang License: GPL-2 Last published: 2024-06-27