KRLS: Kernel-Based Regularized Least Squares

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).

Version:	1.0-0
Suggests:	lattice
Published:	2017-07-10
Author:	Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Maintainer:	Jens Hainmueller <jhain at stanford.edu>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://www.r-project.org, https://www.stanford.edu/~jhain/
NeedsCompilation:	no
Citation:	KRLS citation info
CRAN checks:	KRLS results

Documentation:

Reference manual:

KRLS.pdf

Downloads:

Package source:	KRLS_1.0-0.tar.gz
Windows binaries:	r-devel: KRLS_1.0-0.zip, r-release: KRLS_1.0-0.zip, r-oldrel: KRLS_1.0-0.zip
macOS binaries:	r-release (arm64): KRLS_1.0-0.tgz, r-oldrel (arm64): KRLS_1.0-0.tgz, r-release (x86_64): KRLS_1.0-0.tgz, r-oldrel (x86_64): KRLS_1.0-0.tgz
Old sources:	KRLS archive

Reverse dependencies:

Reverse suggests:

fscaret

Linking:

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