KRLS: Kernel-Based Regularized Least Squares (original) (raw)
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 |
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Suggests: | lattice |
Published: | 2017-07-10 |
DOI: | 10.32614/CRAN.package.KRLS |
Author: | Jens Hainmueller (Stanford) Chad Hazlett (UCLA) |
Maintainer: | Jens Hainmueller |
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 |
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