KRLS: Kernel-based Regularized Least squares (KRLS)

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).

AuthorJens Hainmueller (Stanford) Chad Hazlett (UCLA)
Date of publication2014-05-21 21:21:47
MaintainerJens Hainmueller <jhain@stanford.edu>
LicenseGPL (>= 2)
Version0.3-7
http://www.r-project.org
http://www.stanford.edu/~jhain/

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