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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 |
| Reference manual: | KRLS.pdf |
| 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 suggests: | fscaret |
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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.