Nonparametric kernel distribution function estimation is
performed. Three automatic bandwidth selection methods for
nonparametric kernel distribution function estimation are
implemented: the plug-in of Altman and Leger, the plug-in of
Polansky and Baker, and the modified cross-validation of
Bowman, Hall and Prvan. The exceedance function, the mean
return period and the return level are also computed.

semi-parametric kernel-based approach to local fdr estimations
M Guedj <mickael.guedj@gmail.com> and G Nuel <Gregory.Nuel@math-info.univ-paris5.fr>, with contributions from S Robin and A CelisseA.
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 instance).

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

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

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

DiceKriging, DiceDesign, lhs, inline, foreach

Rcpp (>= 0.10.5), methods, testthat, nloptr
DiceKriging, DiceDesign, lhs, inline, foreach, knitr,
ggplot2, reshape2, corrplot
MASS, numDeriv, stats4, doParallel, doFuture, utils
'CovFormulas.R' 'allGenerics.R' 'checkGrad.R' 'covComp.R'
'covMan.R' 'covQual.R' 'q1CompSymm.R' 'q1Symm.R'
'covQualNested.R' 'covRadial.R' 'covTS.R' 'covZZAll.R' 'gp.R'
'kFuns.R' 'kernelNorm.R' 'kernels1d_Call.R' 'logLikFuns.R'
'methodGLS.R' 'methodMLE.R' 'miscUtils.R' 'prinKrige.R'
'q1Diag.R' 'simulPar.R' 'simulate_gp.R'

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.
Rcpp (>= 0.10.5), methods, testthat, nloptr, lattice
'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'
'covZZAll.R' 'gp.R' 'kFuns.R' 'kernelNorm.R' 'kernels1d_Call.R'
'logLikFuns.R' 'methodGLS.R' 'methodMLE.R' 'miscUtils.R'
'prinKrige.R' 'q1Diag.R' 'simulPar.R' 'simulate_gp.R'
'warpFuns.R'