DESCRIPTION

Package: KRLS2
Type: Package
Title: Kernel-based Regularized Least squares
Version: 1.1.1
Date: 2018-02-08
Author: Jens Hainmueller (Stanford) Chad Hazlett (UCLA) Luke Sonnet (UCLA) (forked by Xinkun Nie with minor changes)
Maintainer: Jens Hainmueller <jhain@stanford.edu> 
Description: Note: this version of the package is forked by Xinkun Nie with minor fixes to the original packages. 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). 
Imports:
    Rcpp (>= 0.12.5),
    RSpectra
LinkingTo: Rcpp, RcppArmadillo
License: GPL (>= 2)
Suggests:
    testthat,
    lattice
URL: https://www.r-project.org, https://www.stanford.edu/~jhain/
RoxygenNote: 6.1.1