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mpath (version 0.4-2.21)

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.packages('mpath')

Monthly Downloads

569

Version

0.4-2.21

License

GPL-2

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Repository

https://github.com/zhuwang46/mpath

Maintainer

Zhu Wang

Last Published

February 9th, 2022

Functions in mpath (0.4-2.21)

fit case weighted support vector machines with robust loss functions

Bread for Sandwiches in Regularized Estimators

Breast feeding decision

conduct backward stepwise variable elimination for zero inflated count regression

Fit a penalized CC-estimator

Weight value from concave function

fit a CC-estimator for robust generalized linear models

Internal function for penalized CC-estimators

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

Compute concave function values

Cross-validation for ccsvm

cv.ccglmreg_fit

Internal function of cross-validation for ccglmreg

Cross-validation for ccglmreg

Internal function of cross-validation for glmreg

Cross-validation for nclreg

Convert response value to raw prediction in GLM

Extract Empirical First Derivative of Log-likelihood Function

Cross-validation for zipath

convert zeroinfl object to class zipath

convert glm object to class glmreg

Cross-validation for zipath

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

Internal function of cross-validation for nclreg

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

Internal function of cross-validation for ccsvm

Composite Loss Value

Cross-validation for glmreg

standardize variables

Cross-validation for glmregNB

Methods for mpath Objects

Standard Error of Regularized Estimators

Internal mpath functions

Meat Matrix Estimator

Composite Loss Value for GLM

summary.glmregNB

Summary Method Function for Objects of Class 'glmregNB'

Composite Loss Value for epsilon-insensitive Type

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

find optimal path for penalized zero-inflated model

Compute weight value

Hessian Matrix of Regularized Estimators

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

Optimize a nonconvex loss with regularization

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

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

Internal function to fit a nonconvex loss based robust linear model

Making Sandwiches with Bread and Meat for Regularized Estimators

random number generation of zero-inflated count response

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

Model predictions based on a fitted "glmreg" object.

Methods for zipath Objects