Ordered Homogeneity Pursuit Lasso for Group Variable Selection • OHPL

Introduction

Implements the ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) <DOI:10.1016/j.chemolab.2017.07.004> (PDF). The OHPL method exploits the homogeneity structure in high-dimensional data and enjoys the grouping effect to select groups of important variables automatically. This feature makes it particularly useful for high-dimensional datasets with strongly correlated variables, such as spectroscopic data.

Paper citation

Formatted citation:

You-Wu Lin, Nan Xiao, Li-Li Wang, Chuan-Quan Li, and Qing-Song Xu (2017). Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data. Chemometrics and Intelligent Laboratory Systems 168, 62-71.

BibTeX entry:

@article{lin2017ordered,
  title   = {Ordered homogeneity pursuit lasso for group variable selection with applications to spectroscopic data},
  author  = {You-Wu Lin and Nan Xiao and Li-Li Wang and Chuan-Quan Li and Qing-Song Xu},
  journal = {Chemometrics and Intelligent Laboratory Systems},
  year    = {2017},
  volume  = {168},
  pages   = {62--71},
  doi     = {10.1016/j.chemolab.2017.07.004}
}

Installation

You can install OHPL from CRAN:

install.packages("OHPL")

Or try the development version on GitHub:

# install.packages("remotes")
remotes::install_github("nanxstats/OHPL")

To get started, try the examples in OHPL():

library("OHPL")
?OHPL

Browse the package documentation for more information.

Contribute

To contribute to this project, please take a look at the Contributing Guidelines first. Please note that the OHPL project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.