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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.
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"
}
To download and install OHPL
from CRAN:
install.packages("OHPL")
Or try the development version on GitHub:
# install.packages("devtools")
::install_github("nanxstats/OHPL") devtools
To get started, try the examples in OHPL()
:
library("OHPL")
?OHPL
Browse the package documentation for more information.
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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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