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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 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:

Please use the canonical form https://CRAN.R-project.org/package=KRLS to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.