BASIS

<p>This is a 1000x481 sample feature matrix</p>

datasets

Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects. Please reference its use as A Huang and D Liu (2016) <doi: 10.1093/bioinformatics/btw143>.

Anhui Huang

EBglmnet

Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Linear Models

BASIS function

<p>The format is:
 int [1:1000, 1:481] 0 -1 0 0 1 0 1 0 1 0 ...</p>

BASIS: An Example Data

Description

Usage

Arguments

Format

Details

Examples