(KRLS) as described in Hainmueller
and Hazlett (2013). KRLS is a
machine learning method that can flexibly
fit solution surfaces of the form y=f(X)
that arise in regression or classification
problems without relying on linearity or
other assumptions that use the columns of
the predictor matrix X directly as basis
functions (such as additivity). KRLS finds
the best fitting function by minimizing a
Tikhonov regularization problem with a
square loss using Gaussian Kernels as
radial basis functions.
KRLS is currently
available for R and Stata. Feedback from
users is appreciated.
KRLS for R
You can
obtain the KRLS package for R from CRAN
by typing:
install.packages("KRLS")
Source: http://cran.r-project.org/web/packages/KRLS/
KRLS for Stata
You
can obtain the krls package for
Stata from SSC by typing:
ssc install krls, all replace
Ferwerda,
Hainmueller, and Hazlett (2013)
describes the Stata package in detail
and provides empirical illustrations.
