KRLS: 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).

Getting started

Package details

AuthorJens Hainmueller (Stanford) Chad Hazlett (UCLA)
MaintainerJens Hainmueller <[email protected]>
LicenseGPL (>= 2)
Version1.0-0
URL https://www.r-project.org https://www.stanford.edu/~jhain/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("KRLS")

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KRLS documentation built on May 2, 2019, 5:51 a.m.