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 | |
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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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[](http://www.rdocumentation.org/packages/KRLS)