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KRLS
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
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[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 03 Feb. 2025.
Previous versions and publish date:
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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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