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FarmSelect  

Factor Adjusted Robust Model Selection
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("FarmSelect", "1.0.2")



Attach the package and use:
library("FarmSelect")
Maintained by
Koushiki Bose
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-01-17
Latest Update: 2018-04-19
Description:
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) , for detailed description of methods and further references.
How to cite:
Koushiki Bose (2018). FarmSelect: Factor Adjusted Robust Model Selection. R package version 1.0.2, https://cran.r-project.org/web/packages/FarmSelect. Accessed 03 Dec. 2024.
Previous versions and publish date:
1.0.0 (2018-01-17 13:56), 1.0.1 (2018-01-30 19:07)
Other packages that cited FarmSelect R package
View FarmSelect citation profile
Other R packages that FarmSelect depends, imports, suggests or enhances
Complete documentation for FarmSelect
Functions, R codes and Examples using the FarmSelect R package
Some associated functions: FarmSelect . farm.res . farm.select . print.farm.select . 
Some associated R codes: FarmSelect-package.R . RcppExports.R . main_funcs.R .  Full FarmSelect package functions and examples
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