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mpath (version 0.3-12)

Regularized Linear Models

Description

Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and 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) .

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Version

Install

install.packages('mpath')

Monthly Downloads

611

Version

0.3-12

License

GPL-2

Maintainer

Zhu Wang

Last Published

April 15th, 2019

Functions in mpath (0.3-12)

fit a nonconvex loss based robust linear model

Methods for mpath Objects

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

random number generation of zero-inflated count response

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

Making Sandwiches with Bread and Meat for Regularized Estimators

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

Extract Empirical First Derivative of Log-likelihood Function

Internal mpath functions

Hessian Matrix of Regularized Estimators

Cross-validation for glmregNB

Meat Matrix Estimator

Standard Error of Regularized Estimators

standardize variables

Internal function to fit a nonconvex loss based robust linear model

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

plot coefficients from a "glmreg" object

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

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

summary.glmregNB

Summary Method Function for Objects of Class 'glmregNB'

Model predictions based on a fitted "glmreg" object.

find optimal path for penalized zero-inflated model

Methods for zipath Objects

Internal function of cross-validation for glmreg

Cross-validation for nclreg

convert glm object to class glmreg

convert zeroinfl object to class zipath

Cross-validation for glmreg

Internal function of cross-validation for nclreg

Cross-validation for zipath

conduct backward stepwise variable elimination for zero inflated count regression

Bread for Sandwiches in Regularized Estimators