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
DOI: 10.32614/CRAN.package.KRLS
Author: Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Maintainer: Jens Hainmueller <jhain at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: KRLS citation info
CRAN checks: KRLS results


Reference manual: KRLS.pdf


Package source: KRLS_1.0-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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