Authors@R: c(person(given = "Kridsadakorn", family = "Chaichoompu", email = "kridsadakorn@biostatgen.org", role = c("aut", "cre")),person(given = "Kristel", family = 'Van Steen', role = "aut"),person(given = "Fentaw", family = "Abegaz", role = "aut"),person(given = "Sissades", family = "Tongsima", role = "aut"),person(given = "Philip", family = "James Shaw", role = "aut"),person(given = "Anavaj", family = "Sakuntabhai", role = "aut"),person(given = "Luisa", family = "Pereira", role = "aut"))
Packaged: 2018-06-04 12:10:43 UTC; kridsadakorn

Title: Kernel-based Regularized Least Squares (KRLS)
Author: Jens Hainmueller (MIT) Chad Hazlett (MIT)
Description: Package implements Kernel-based Regularized Least Squares
(KRLS), a machine learning method that can be used to fit
multidimensional functions for regression and classification
problems without relying on linearity or additivity
assumptions. This package is currently in alpha phase (feedback
Packaged: 2011-09-29 16:44:49 UTC; jhainmueller

Description: Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method that can be used to fit multidimensional functions for regression and classification problems without relying on linearity or additivity assumptions. This package is currently in alpha phase (feedback is appreciated).
Packaged: 2011-12-26 21:48:32 UTC; jhainmueller

Description: Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions for regression and classification problems without relying on linearity or additivity assumptions. This package is currently in alpha phase (feedback is appreciated).
Packaged: 2013-08-07 14:48:28 UTC; jhainmueller

Packaged: 2013-08-09 21:56:44 UTC; jhainmueller

Title: Kernel-based Regularized Least squares (KRLS)
Author: Jens Hainmueller (Stanford) Chad Hazlett (MIT)
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).
Packaged: 2013-12-19 19:51:51 UTC; jhainmueller