KRLS: Kernel-based Regularized Least squares (KRLS)

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: 0.3-7
Suggests: lattice
Published: 2014-05-21
Author: Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Maintainer: Jens Hainmueller <jhain at stanford.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.r-project.org, http://www.stanford.edu/~jhain/
NeedsCompilation: no
CRAN checks: KRLS results

Downloads:

Reference manual: KRLS.pdf
Package source: KRLS_0.3-7.tar.gz
Windows binaries: r-devel: KRLS_0.3-7.zip, r-release: KRLS_0.3-7.zip, r-oldrel: KRLS_0.3-7.zip
OS X Snow Leopard binaries: r-release: KRLS_0.3-7.tgz, r-oldrel: KRLS_0.3-7.tgz
OS X Mavericks binaries: r-release: KRLS_0.3-7.tgz
Old sources: KRLS archive

Reverse dependencies:

Reverse suggests: fscaret