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OHPL
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]
[Scholar Profile | Author Map]
All associated links for this package
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 22 Dec. 2024.
Previous versions and publish date:
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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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