Author: M Guedj <mickael.guedj@gmail.com> and G Nuel <Gregory.Nuel@math-info.univ-paris5.fr>, with contributions from S Robin and A Celisse.

Author: M Guedj <mickael.guedj@gmail.com> and G Nuel
Description: semi-parametric kernel-based approaches to local fdr
estimations useful for the testing of multiple hypothesis (in
large-scale genetic, genomic and post-genomic studies for

from S Robin, A Celisse and ML Martin-Migniette.

Suggests: DiceKriging, DiceDesign, lhs, inline, foreach
Imports: MASS, numDeriv, stats4, doParallel

Depends: Rcpp (>= 0.10.5), methods, testthat, nloptr
Suggests: DiceKriging, DiceDesign, lhs, inline, foreach, knitr,
Imports: MASS, numDeriv, stats4, doParallel, doFuture, utils
Collate: 'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R'
'covQualNested.R' 'covRadial.R' 'covTS.R' 'covZZAll.R' 'gp.R'

Description: 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,
Depends: Rcpp (>= 0.10.5), methods, testthat, nloptr, lattice
'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R' 'q1LowRank.R'
'covZZAll.R' 'gp.R' 'kFuns.R' 'kernelNorm.R' 'kernels1d_Call.R'

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
Suggests: DiceKriging, DiceDesign, inline, foreach, knitr, ggplot2,
'covTP.R' 'covANOVA.R' 'covZZAll.R' 'gp.R' 'kFuns.R'

Author: Thomas Welchowski <welchow@imbie.meb.uni-bonn.de> and Matthias Schmid
Description: 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.
Imports: methods, Rcpp, glmnet, mvtnorm, lhs, DiceKriging, parallel,