covr, ggplot2, gridExtra, reshape2, zoo, testthat

covr, fda, ggplot2 (>= 3.0.0), gridExtra, reshape2, zoo,
testthat (<= 2.0.0)
'funDataClass.R' 'coerce.R' 'funDataMethods.R' 'get_set.R'
'names.R' 'plotMethods.R' 'simulation.R' 'str.R' 'subset.R'
'summary.R' 'zzz.R'

covr, fda, ggplot2 (>= 3.0.0), gridExtra, reshape2, zoo,
testthat (>= 2.0.0)

person("Clara", "Happ-Kurz", email = "clara.happ@stat.uni-muenchen.de",
role = c("aut", "cre"))

S4 classes for univariate and multivariate functional data with
utility functions. See <doi:10.18637/jss.v093.i05> for a detailed description
of the package functionalities and its interplay with the MFPCA package for
multivariate functional principal component analysis
<https://CRAN.R-project.org/package=MFPCA>.

person("Clara", "Happ-Kurz", email = "chk_R@gmx.de",
role = c("aut", "cre"))

covr, fda, ggplot2 (>= 3.0.0), gridExtra, reshape2, zoo,
testthat

Clustering in the Discriminative Functional Subspace
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Gaussian Process Models for Scalar and Functional Inputs
c(
person("Jose", "Betancourt", email = "djbetancourt@uninorte.edu.co", role = c("cre", "aut")),
person("François", "Bachoc", email = "francois.bachoc@math.univ-toulouse.fr", role = "aut"),
person("Thierry", "Klein", email = "thierry.klein@math.univ-toulouse.fr", role = "aut"),
person("Deborah", "Idier", email = "D.Idier@brgm.fr", role = "ctb"),
person("Jeremy", "Rohmer", email = "J.Rohmer@brgm.fr", role = "ctb"))
Construction and smart selection of Gaussian process models
with emphasis on treatment of functional inputs. This package
offers: (i) flexible modeling of functional-input regression
problems through the fairly general Gaussian process model; (ii)
built-in dimension reduction for functional inputs; (iii)
heuristic optimization of the structural parameters of the model
(e.g., active inputs, kernel function, type of distance).
Metamodeling background is provided in
Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>.
The algorithm for structural parameter optimization is described
in <https://hal.archives-ouvertes.fr/hal-02532713>.
research product of the RISCOPE project (ANR, project
No.16CE04-0011) <https://perso.math.univ-toulouse.fr/riscope/>.
methods, foreach, knitr, scales, qdapRegex, microbenchmark,
doFuture, future, progressr
'0_funGp_Doc.R' '0_show_Doc.R' '8_outilsCode.R'
'3_ant_admin.R' '2_fgpKern_Class.R' '2_fgpProj_Class.R'
'1_fgpm_Class.R' '1_Xfgpm_Class.R' '3_ant_search.R'
'3_training_F.R' '3_training_S.R' '3_training_SF.R'
'4_prediction_F.R' '4_prediction_S.R' '4_prediction_SF.R'
'5_simulation_F.R' '5_simulation_S.R' '5_simulation_SF.R'
'6_updating.R' '7_blackBoxFunctions.R' '7_checkingFunctions.R'
'7_correlFunctions.R' '7_dimRedFunctions.R'
'7_distanceFunctions.R' '7_plottingFunctions.R'
'8_outilsStats.R'
Jose Betancourt [cre, aut],
François Bachoc [aut],
Thierry Klein [aut],
Deborah Idier [ctb],
Jeremy Rohmer [ctb]