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mpath (version 0.3-26)

Regularized Linear Models

Description

Algorithms optimize penalized models. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), and each possibly combining with L_2 penalty. See Wang et al. (2014) , Wang et al. (2015) , Wang et al. (2016) , Wang (2019) .

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Install

install.packages('mpath')

Monthly Downloads

433

Version

0.3-26

License

GPL-2

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Repository

https://github.com/zhuwang46/mpath

Maintainer

Zhu Wang

Last Published

June 1st, 2020

Functions in mpath (0.3-26)

Internal function of cross-validation for glmreg

convert glm object to class glmreg

convert zeroinfl object to class zipath

conduct backward stepwise variable elimination for zero inflated count regression

Cross-validation for glmreg

Cross-validation for glmregNB

Cross-validation for nclreg

Internal function of cross-validation for nclreg

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

Internal function to fit a nonconvex loss based robust linear model with lasso (or elastic net), snet and mnet regularization

Bread for Sandwiches in Regularized Estimators

Meat Matrix Estimator

Making Sandwiches with Bread and Meat for Regularized Estimators

Cross-validation for zipath

Methods for mpath Objects

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

Hessian Matrix of Regularized Estimators

fit a nonconvex loss based robust linear model

Internal mpath functions

Extract Empirical First Derivative of Log-likelihood Function

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

standardize variables

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

find optimal path for penalized zero-inflated model

Cross-validation for zipath

plot coefficients from a "glmreg" object

fit a nonconvex loss based robust linear model with lasso (or elastic net), snet or mnet regularization

summary.glmregNB

Summary Method Function for Objects of Class 'glmregNB'

Model predictions based on a fitted "glmreg" object.

Standard Error of Regularized Estimators

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

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

Methods for zipath Objects

Internal function to fit a nonconvex loss based robust linear model

random number generation of zero-inflated count response