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, <doi:10.1093/pan/mpt019>).
| Version: | 1.1-0 |
| Imports: | grDevices, graphics, stats |
| Suggests: | lattice, testthat (≥ 3.0.0) |
| Published: | 2026-04-30 |
| DOI: | 10.32614/CRAN.package.KRLS |
| Author: | Jens Hainmueller [aut, cre], Chad Hazlett [aut] |
| Maintainer: | Jens Hainmueller <jhain at stanford.edu> |
| BugReports: | https://github.com/j-hai/KRLS/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://web.stanford.edu/~jhain/, https://github.com/j-hai/KRLS |
| NeedsCompilation: | no |
| Citation: | KRLS citation info |
| Materials: | NEWS |
| CRAN checks: | KRLS results |
| Reference manual: | KRLS.html , KRLS.pdf |
| Package source: | KRLS_1.1-0.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_1.1-0.tgz, r-oldrel (arm64): KRLS_1.1-0.tgz, r-release (x86_64): KRLS_1.0-0.tgz, r-oldrel (x86_64): KRLS_1.0-0.tgz |
| Old sources: | KRLS archive |
| Reverse imports: | InfluenceBorrowing |
Please use the canonical form https://CRAN.R-project.org/package=KRLS to link to this page.