Package: KRLS 1.7-1

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, <doi:10.1093/pan/mpt019>).

Authors:Jens Hainmueller [aut, cre], Chad Hazlett [aut]