KRLS v1.0-0
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Kernel-Based Regularized Least Squares
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).
Functions in KRLS
| Name | Description | |
| krls | Kernel-based Regularized Least Squares (KRLS) | |
| lambdasearch | Leave-one-out optimization to find \(\lambda\) | |
| summary.krls | Summary method for Kernel-based Regularized Least Squares (KRLS) Model Fits | |
| fdskrls | Compute first differences with KRLS | |
| predict.krls | Predict method for Kernel-based Regularized Least Squares (KRLS) Model Fits | |
| solveforc | Solve for Choice Coefficients in KRLS | |
| looloss | Loss Function for Leave One Out Error | |
| plot.krls | Plot method for Kernel-based Regularized Least Squares (KRLS) Model Fits | |
| gausskernel | Gaussian Kernel Distance Computation | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2017-07-08 |
| License | GPL (>= 2) |
| URL | https://www.r-project.org, https://www.stanford.edu/~jhain/ |
| NeedsCompilation | no |
| Packaged | 2017-07-10 05:24:25 UTC; chad |
| Repository | CRAN |
| Date/Publication | 2017-07-10 13:55:59 UTC |
| suggests | lattice |
| Contributors | Jens Chad Hazlett |
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