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KRLS  

Kernel-Based Regularized Least Squares
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("KRLS", "1.0-0")



Attach the package and use:
library("KRLS")
Maintained by
Jens Hainmueller
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2011-09-30
Latest Update: 2017-07-10
Description:
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
How to cite:
Jens Hainmueller (2011). KRLS: Kernel-Based Regularized Least Squares. R package version 1.0-0, https://cran.r-project.org/web/packages/KRLS. Accessed 25 Jun. 2026.
Previous versions and publish date:
0.1 (2011-09-30 07:51), 0.2 (2011-12-27 11:05), 0.3-1 (2013-08-07 17:56), 0.3-2 (2013-08-10 00:53), 0.3-5 (2013-12-20 07:20), 0.3-7 (2014-05-21 21:21), 1.0-0 (2017-07-10 15:56), 1.1-0 (2026-04-30 07:10)
Other packages that cited KRLS R package
View KRLS citation profile
Other R packages that KRLS depends, imports, suggests or enhances
Complete documentation for KRLS
Functions, R codes and Examples using the KRLS R package
Some associated functions: fdskrls . gausskernel . krls . lambdasearch . looloss . plot.krls . predict.krls . solveforc . summary.krls . 
Some associated R codes: gausskernel.R . krls.R . looloss.R . multdiag.R . plot.krls.R . predict.krls.R . solveforc.R . summary.krls.R .  Full KRLS package functions and examples
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