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gKRLS  

Generalized Kernel Regularized Least Squares
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("gKRLS", "1.0.4")



Attach the package and use:
library("gKRLS")
Maintained by
Max Goplerud
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-04-17
Latest Update: 2024-11-07
Description:
Kernel regularized least squares, also known as kernel ridge regression, is a flexible machine learning method. This package implements this method by providing a smooth term for use with 'mgcv' and uses random sketching to facilitate scalable estimation on large datasets. It provides additional functions for calculating marginal effects after estimation and for use with ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2023) provide further details.
How to cite:
Max Goplerud (2023). gKRLS: Generalized Kernel Regularized Least Squares. R package version 1.0.4, https://cran.r-project.org/web/packages/gKRLS. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 07:40), 1.0.1 (2023-04-17 12:30), 1.0.2 (2023-04-20 11:20), 1.0.3 (2024-09-04 16:00)
Other packages that cited gKRLS R package
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Complete documentation for gKRLS
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