https://github.com/nanxstats/ohpl
📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection
https://github.com/nanxstats/ohpl
chemometrics high-dimensional-data homogeneity-pursuit lasso partial-least-squares-regression spectroscopy variable-selection
Last synced: 4 days ago
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📈 Ordered Homogeneity Pursuit Lasso for Group Variable Selection
- Host: GitHub
- URL: https://github.com/nanxstats/ohpl
- Owner: nanxstats
- License: gpl-3.0
- Created: 2017-05-12T04:22:18.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-07-21T18:30:30.000Z (9 months ago)
- Last Synced: 2025-04-10T07:18:48.591Z (16 days ago)
- Topics: chemometrics, high-dimensional-data, homogeneity-pursuit, lasso, partial-least-squares-regression, spectroscopy, variable-selection
- Language: R
- Homepage: https://OHPL.io
- Size: 4.01 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Ordered Homogeneity Pursuit Lasso 
[](https://github.com/nanxstats/OHPL/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=OHPL)
[](https://cran.r-project.org/package=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](https://doi.org/10.1016/j.chemolab.2017.07.004)> ([PDF](https://nanx.me/papers/OHPL.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:
```r
install.packages("OHPL")
```
Or try the development version on GitHub:
```r
# install.packages("remotes")
remotes::install_github("nanxstats/OHPL")
```
To get started, try the examples in `OHPL()`:
```r
library("OHPL")
?OHPL
```
[Browse the package documentation](https://ohpl.io/doc/) for more information.
## Contribute
To contribute to this project, please take a look at the [Contributing Guidelines](https://ohpl.io/doc/CONTRIBUTING.html) first.
Please note that the OHPL project is released with a
[Contributor Code of Conduct](https://ohpl.io/doc/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.