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.