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mpath (version 0.4-2.20)

Regularized Linear Models

Description

Algorithms compute concave convex (CC) estimators including robust (penalized) generalized linear models and robust support vector machines via the COCO - composite optimization by conjugation operator. 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) , Wang et al. (2015) , Wang et al. (2016) , Wang (2021) , Wang (2020) .

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Install

install.packages('mpath')

Monthly Downloads

433

Version

0.4-2.20

License

GPL-2

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Repository

https://github.com/zhuwang46/mpath

Maintainer

Zhu Wang

Last Published

December 3rd, 2021

Functions in mpath (0.4-2.20)

Weight value from concave function

Internal function for penalized CC-estimators

Fit iteratively re-weighted support vector machines for robust loss functions

convert glm object to class glmreg

Cross-validation for ccglmreg

convert zeroinfl object to class zipath

cv.ccglmreg_fit

Internal function of cross-validation for ccglmreg

Internal function of cross-validation for glmreg

Cross-validation for glmregNB

Internal function of cross-validation for ccsvm

Cross-validation for glmreg

Cross-validation for ccsvm

Cross-validation for nclreg

Extract Empirical First Derivative of Log-likelihood Function

Cross-validation for zipath

Internal function of cross-validation for nclreg

fit a GLM with lasso (or elastic net), snet or mnet regularization

fit a negative binomial model with lasso (or elastic net), snet and mnet regularization

Hessian Matrix of Regularized Estimators

Internal function to fit a GLM with lasso (or elastic net), snet and mnet regularization

Composite Loss Value for GLM

Meat Matrix Estimator

Composite Loss Value

Composite Loss Value for epsilon-insensitive Type

Convert response value to raw prediction in GLM

Internal function to fitting a nonconvex loss based robust linear model with regularization

Optimize a nonconvex loss with regularization

Cross-validation for zipath

standardize variables

Standard Error of Regularized Estimators

Internal mpath functions

Methods for mpath Objects

Model predictions based on a fitted "glmreg" object.

random number generation of zero-inflated count response

Internal function to fit a nonconvex loss based robust linear model

plot coefficients from a "glmreg" object

compute p-values from penalized zero-inflated model with multi-split data

fit a nonconvex loss based robust linear model

Methods for zipath Objects

Internal function to fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization

summary.glmregNB

Summary Method Function for Objects of Class 'glmregNB'

find optimal path for penalized zero-inflated model

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

Making Sandwiches with Bread and Meat for Regularized Estimators

Compute weight value

fit a CC-estimator for robust generalized linear models

Breast feeding decision

fit case weighted support vector machines with robust loss functions

conduct backward stepwise variable elimination for zero inflated count regression

Compute concave function values

Bread for Sandwiches in Regularized Estimators

Fit a penalized CC-estimator