Gaussian process regression with an emphasis on kernels.
Quantitative and qualitative inputs are accepted. Some pre-defined
kernels are available, such as radial or tensor-sum for
quantitative inputs, and compound symmetry, low rank, group kernel
for qualitative inputs. The user can define new kernels and
composite kernels through a formula mechanism. Useful methods
include parameter estimation by maximum likelihood, simulation,
prediction and leave-one-out validation.
DiceKriging, DiceDesign, inline, foreach, knitr, ggplot2,
reshape2, corrplot
'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R'
'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R' 'q1LowRank.R'
'covQualNested.R' 'covQualOrd.R' 'covRadial.R' 'covTS.R'
'covTP.R' 'covANOVA.R' 'covZZAll.R' 'gp.R' 'kFuns.R'
'kernelNorm.R' 'kernels1d_Call.R' 'logLikFuns.R' 'methodGLS.R'
'methodMLE.R' 'miscUtils.R' 'prinKrige.R' 'q1Diag.R'
'simulate_gp.R' 'warpFuns.R'

Thomas Welchowski <welchow@imbie.meb.uni-bonn.de> and Matthias Schmid
<matthias.schmid@imbie.uni-bonn.de>
Contains functions for estimation and model selection of kernel
deep stacking networks. The model selection includes direct optimization or
model based alternatives with arbitrary loss functions.
methods, Rcpp, glmnet, mvtnorm, lhs, DiceKriging, parallel,
globalOptTests

methods, Rcpp, glmnet, mvtnorm, lhs, DiceOptim, DiceKriging,
parallel, globalOptTests, GenSA, GA, matrixStats, caret

Contains functions for estimation and model selection of kernel
deep stacking networks.

Distance Weighted Discrimination (DWD) and Kernel Methods
Boxiang Wang <boxiang@umn.edu>, Hui Zou <hzou@stat.umn.edu>
A very efficient implementation that solves the generalized DWD under any kernel regularization.

A very efficient implementation that solves the generalized DWD under any kernel regularization. The package also handles some other general loss functions including LUM.

A novel implementation that solves the DWD and the kernel DWD which can be hundred times faster than the existing implementation of DWD based on the second-order-cone programming (SOCP). The package also handles some other general loss functions including large-margin unified machines (LUM).