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KRLS: Kernel-Based Regularized Least Squares
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>).
Getting started
Browse package contents
Package details |
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| Author | Jens Hainmueller [aut, cre], Chad Hazlett [aut] |
| Maintainer | Jens Hainmueller <jhain@stanford.edu> |
| License | GPL (>= 2) |
| Version | 1.1-0 |
| URL | https://web.stanford.edu/~jhain/ https://github.com/j-hai/KRLS |
| Package repository | View on CRAN |
| Installation |
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