rentrez (>= 1.1.0), data.table, hoardr (>= 0.5.0), pdftools,

Description: A set of functions for the analysis of climate/weather

Author: Yihui Xie, Taiyun Wei and Yixuan Qiu
Description: This is a collection of R games and other funny stuff,
such as the classical Mine sweeper and sliding puzzles.

Description: This is a collection of R games and other funny stuff, such as the
classic Mine sweeper and sliding puzzles.

Description: S4 classes for univariate and multivariate functional data with
Imports: abind, fields, foreach, graphics, grDevices, stats

Suggests: covr, ggplot2, gridExtra, reshape2, zoo, testthat

Suggests: covr, fda, ggplot2 (>= 3.0.0), gridExtra, reshape2, zoo,
Collate: 'funDataClass.R' 'coerce.R' 'funDataMethods.R' 'get_set.R'
'names.R' 'plotMethods.R' 'simulation.R' 'str.R' 'subset.R'

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

Title: Clustering in the Discriminative Functional Subspace
Depends: R (>= 2.10), MASS, fda, elasticnet
Description: The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.

Title: Gaussian Process Models for Scalar and Functional Inputs
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"))
Description: 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).
The algorithm for structural parameter optimization is described
Note: research product of the RISCOPE project (ANR, project
Imports: methods, foreach, knitr, scales, qdapRegex, microbenchmark,