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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