lukesonnet/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).
Getting started
Browse package contents
Package details |
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| Author | Jens Hainmueller (Stanford) Chad Hazlett (UCLA) Luke Sonnet (UCLA) |
| Maintainer | Jens Hainmueller <jhain@stanford.edu> |
| License | GPL (>= 2) |
| Version | 1.1.0 |
| URL | https://www.r-project.org https://www.stanford.edu/~jhain/ |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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