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mpath (version 0.3-7)

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

0.3-7

License

GPL-2

Maintainer

Zhu Wang

Last Published

February 27th, 2019

Functions in mpath (0.3-7)

Internal function of cross-validation for glmreg

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

Cross-validation for nclreg

random number generation of zero-inflated count response

Internal function to fit a nonconvex loss based robust linear model

fit a nonconvex loss based robust linear model

Making Sandwiches with Bread and Meat for Regularized Estimators

Extract Empirical First Derivative of Log-likelihood Function

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

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

Cross-validation for glmreg

Cross-validation for glmregNB

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

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

convert glm object to class glmreg

convert zeroinfl object to class zipath

standardize variables

Hessian Matrix of Regularized Estimators

Meat Matrix Estimator

Standard Error of Regularized Estimators

Model predictions based on a fitted "glmreg" object.

Methods for zipath Objects

summary.glmregNB

Summary Method Function for Objects of Class 'glmregNB'

find optimal path for penalized zero-inflated model

Methods for mpath Objects

Internal mpath functions

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

plot coefficients from a "glmreg" object

conduct backward stepwise variable elimination for zero inflated count regression

Bread for Sandwiches in Regularized Estimators

Internal function of cross-validation for nclreg

Cross-validation for zipath

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