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TPCselect  

Variable Selection via Threshold Partial Correlation
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("TPCselect", "0.8.3")



Attach the package and use:
library("TPCselect")
Maintained by
Cynthia Shao
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-09-15
Latest Update: 2023-06-29
Description:
A threshold partial correlation approach to selecting important variables in linear models of L. and others (2017) at <doi:10.5705/ss.202015.0473>, and in partial linear models of L. and others (2018) at <doi:10.1016/j.jmva.2018.06.005>. This package also extends the PC-simple algorithm of B. and others (2010) at <doi:10.1093/biomet/asq008> to partial linear models.
How to cite:
Cynthia Shao (2022). TPCselect: Variable Selection via Threshold Partial Correlation. R package version 0.8.3, https://cran.r-project.org/web/packages/TPCselect. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.8.1 (2022-09-15 10:00)
Other packages that cited TPCselect R package
View TPCselect citation profile
Other R packages that TPCselect depends, imports, suggests or enhances
Complete documentation for TPCselect
Functions, R codes and Examples using the TPCselect R package
Some associated functions: TPC . TPC_BIC . TPC_pl . TPC_pl_BIC . generate_toy_pldata . 
Some associated R codes: TPC.R . TPC_BIC.R . TPC_pl.R . TPC_pl_BIC.R . utils.R .  Full TPCselect package functions and examples
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