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

Downloads:

Reference manual: KRLS.pdf
Package source: KRLS_1.0-0.tar.gz
Windows binaries: r-prerel: KRLS_1.0-0.zip, r-release: KRLS_1.0-0.zip, r-oldrel: KRLS_1.0-0.zip
OS X binaries: r-prerel: KRLS_1.0-0.tgz, r-release: KRLS_1.0-0.tgz
Old sources: KRLS archive

Reverse dependencies:

Reverse suggests: fscaret

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