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README.md

Ordered Homogeneity Pursuit Lasso (OHPL) logo

Travis-CI Build Status AppVeyor Build Status CRAN Version Downloads from the RStudio CRAN mirror

Introduction

OHPL 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. https://doi.org/10.1016/j.chemolab.2017.07.004

BibTeX entry:

@article{Lin2017,
  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",
  issn = "0169-7439",
  doi = "https://doi.org/10.1016/j.chemolab.2017.07.004",
  url = "http://www.sciencedirect.com/science/article/pii/S0169743917300503"
}

Installation

To download and install OHPL from CRAN:

install.packages("OHPL")

Or try the development version on GitHub:

# install.packages("devtools")
devtools::install_github("road2stat/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 this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.