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OHPL  

Ordered Homogeneity Pursuit Lasso for Group Variable Selection
View on CRAN: Click here


Download and install OHPL package within the R console
Install from CRAN:
install.packages("OHPL")

Install from Github:
library("remotes")
install_github("cran/OHPL")

Install by package version:
library("remotes")
install_version("OHPL", "1.4.1")



Attach the package and use:
library("OHPL")
Maintained by
Nan Xiao
[Scholar Profile | Author Map]
First Published: 2017-07-17
Latest Update: 2019-05-18
Description:
Ordered homogeneity pursuit lasso (OHPL) algorithm for group variable selection proposed in Lin et al. (2017) . 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.
How to cite:
Nan Xiao (2017). OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection. R package version 1.4.1, https://cran.r-project.org/web/packages/OHPL. Accessed 29 Mar. 2025.
Previous versions and publish date:
1.2 (2017-07-17 11:44), 1.3 (2017-08-08 19:19), 1.4 (2019-05-18 06:10)
Other packages that cited OHPL R package
View OHPL citation profile
Other R packages that OHPL depends, imports, suggests or enhances
Complete documentation for OHPL
Functions, R codes and Examples using the OHPL R package
Some associated functions: FOP . OHPL-package . OHPL.RMSEP . OHPL . OHPL.sim . beer . cv.OHPL . dlc . predict.OHPL . proto . soil . wheat . 
Some associated R codes: FOP.R . OHPL-datalist.R . OHPL-package.R . OHPL.R . OHPL.RMSEP.R . OHPL.sim.R . cv.OHPL.R . dia.R . dlc.R . predict.OHPL.R . proto.R . single.beta.R .  Full OHPL package functions and examples
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