Package: KRLS
Type: Package
Title: Kernel-Based Regularized Least Squares
Version: 1.0-0
Date: 2017-07-08
Author: Jens Hainmueller (Stanford) Chad Hazlett (UCLA)
Maintainer: Jens Hainmueller <jhain@stanford.edu>
Description: 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).
License: GPL (>= 2)
Suggests: lattice
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
