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Ohit  

OGA+HDIC+Trim and High-Dimensional Linear Regression Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("Ohit", "1.0.0")



Attach the package and use:
library("Ohit")
Maintained by
Hai-Tang Chiou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-09-06
Latest Update: 2017-09-06
Description:
Ing and Lai (2011) proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.
How to cite:
Hai-Tang Chiou (2017). Ohit: OGA+HDIC+Trim and High-Dimensional Linear Regression Models. R package version 1.0.0, https://cran.r-project.org/web/packages/Ohit. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Other R packages that Ohit depends, imports, suggests or enhances
Complete documentation for Ohit
Functions, R codes and Examples using the Ohit R package
Some associated functions: OGA . Ohit . predict_Ohit . 
Some associated R codes: OGA.R . Ohit.R . predict_Ohit.R .  Full Ohit package functions and examples
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