The R package fda.usc carries out exploratory and descriptive analysis of functional data such as depth measurements or functional atypical curves detection, functions to compute functional regression models with a scalar response, supervised and unsupervised classification methods and functional analysis of variance.

c(person("Manuel Febrero Bande",role="aut", email = "manuel.febrero@usc.es"), person("Manuel Oviedo de la Fuente",role=c("aut","cre"), email="manuel.oviedo@usc.es"),
person("Pedro Galeano",role="ctb"),
person("Alicia Nieto",role="ctb"),
person("Eduardo Garcia-Portugues",role="ctb"))
Manuel Febrero Bande [aut],
Manuel Oviedo de la Fuente [aut, cre],
Pedro Galeano [ctb],
Alicia Nieto [ctb],
Eduardo Garcia-Portugues [ctb]

R (>= 2.10), fda, splines, MASS, mgcv, rpart

Functional Data Analysis and Utilities for Statistical Computing
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

methods, grDevices, graphics, utils, stats

methods, grDevices, graphics, utils, stats, nlme

methods, grDevices, graphics, utils, stats, nlme, doParallel,
parallel, iterators, foreach

c(person("Manuel Febrero Bande",role="aut", email = "manuel.febrero@usc.es"), person("Manuel Oviedo de la Fuente",role=c("aut","cre"), email="manuel.oviedo@udc.es"),
person("Pedro Galeano",role="ctb"),
person("Alicia Nieto",role="ctb"),
person("Eduardo Garcia-Portugues",role="ctb"))
methods, grDevices, graphics, utils, stats, nlme, doParallel,
parallel, iterators, foreach, kSamples
Manuel Oviedo de la Fuente <manuel.oviedo@udc.es>

Autocorrelation Function for Functional Time Series
Guillermo Mestre Marcos [aut, cre],
José Portela González [aut],
Antonio Muñoz San Roque [ctb],
Estrella Alonso Pérez [ctb]
Quantify the serial correlation across lags of a given functional
time series using an autocorrelation function for functional time series.
The autocorrelation function is based on the L2 norm of the lagged covariance
operators of the series. Functions are available for estimating the
distribution of the autocorrelation function under the assumption
of strong functional white noise.
c(
person(given = "Guillermo", family = "Mestre Marcos",
email = "guillermo.mestre@comillas.edu", role = c("aut", "cre")),
person(given = "José", family = "Portela González",
email = "Jose.Portela@iit.comillas.edu", role = "aut"),
person(given = "Antonio", family = "Muñoz San Roque",
email = "antonio.munoz@iit.comillas.edu", role = "ctb"),
person(given = "Estrella", family = "Alonso Pérez",
email = "ealonso@icai.comillas.edu", role = "ctb")
)