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

Package details

AuthorJens Hainmueller (Stanford) Chad Hazlett (UCLA) Luke Sonnet (UCLA)
MaintainerJens Hainmueller <>
LicenseGPL (>= 2)
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
lukesonnet/KRLS documentation built on May 21, 2019, 8:58 a.m.