KRLS: Kernel-based Regularized Least squares (KRLS)
Version 0.3-7

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).

Browse man pages Browse package API and functions Browse package files

AuthorJens Hainmueller (Stanford) Chad Hazlett (UCLA)
Date of publication2014-05-21 21:21:47
MaintainerJens Hainmueller <jhain@stanford.edu>
LicenseGPL (>= 2)
Version0.3-7
URL http://www.r-project.org http://www.stanford.edu/~jhain/
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("KRLS")

Man pages

fdskrls: Compute first differences with KRLS
gausskernel: Gaussian Kernel Distance Computation
krls: Kernel-based Regularized Least Squares (KRLS)
lambdasearch: Leave-one-out optimization to find lambda
looloss: Loss Function for Leave One Out Error
plot.krls: Plot method for Kernel-based Regularized Least Squares (KRLS)...
predict.krls: Predict method for Kernel-based Regularized Least Squares...
solveforc: Solve for Choice Coefficients in KRLS
summary.krls: Summary method for Kernel-based Regularized Least Squares...

Functions

fdskrls Man page Source code
gausskernel Man page Source code
krls Man page Source code
lambdasearch Man page Source code
looloss Man page Source code
multdiag Source code
onAttach Source code
plot.krls Man page Source code
predict.krls Man page Source code
solveforc Man page Source code
summary.krls Man page Source code

Files

NAMESPACE
GPL-2
R
R/gausskernel.R
R/looloss.R
R/solveforc.R
R/multdiag.R
R/lambdasearch.r
R/summary.krls.R
R/predict.krls.R
R/plot.krls.R
R/zzz.r
R/krls.R
R/fdskrls.r
MD5
DESCRIPTION
LICENSE.note
man
man/krls.Rd
man/solveforc.Rd
man/looloss.Rd
man/fdskrls.Rd
man/summary.krls.Rd
man/lambdasearch.Rd
man/plot.krls.Rd
man/predict.krls.Rd
man/gausskernel.Rd
GPL-3
KRLS documentation built on May 19, 2017, 11:25 p.m.