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SMLE (version 0.3.1)

Joint Feature Screening via Sparse MLE

Description

Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Sparse Maximal Likelihood Estimator (SMLE) (Xu and Chen (2014)) provides an efficient implementation for the joint feature screening method on high-dimensional generalized linear models. It also conducts a post-screening selection based on user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.

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Version

Install

install.packages('SMLE')

Monthly Downloads

676

Version

0.3.1

License

GPL-2

Maintainer

Qianxiang Zang

Last Published

May 18th, 2020

Functions in SMLE (0.3.1)

print.selection

Print a selection object from smle_select
plot.smle

Plots to visualize the SMLE screening step
print.sdata

Print function for simulated data
Gen_Data

Data simulator for high-dimensional GLMs
smle-package

Joint SMLE-screening for generalized linear models
print.smle

Print a SMLE object from SMLE
smle_select

Elaborative feature selection with SMLE
SMLE

Joint feature screening via sparse maximum likelihood estimation for GLMs
plot.selection

Plots to visualize the selection steps