Guillermo Mestre Marcos [aut, cre],
José Portela González [aut],
Gregory Rice [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 and a partial autocorrelation
function for functional time series.
The autocorrelation functions are based on the L2 norm of the lagged covariance
operators of the series. Functions are available for estimating the
distribution of the autocorrelation functions 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 = "Gregory", family = "Rice",
email = "grice@uwaterloo.ca", 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")
)

Quantify the serial correlation across lags of a given functional
time series using the autocorrelation function and a partial autocorrelation
function for functional time series proposed in
Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>.
The autocorrelation functions are based on the L2 norm of the lagged covariance
operators of the series. Functions are available for estimating the
distribution of the autocorrelation functions under the assumption
of strong functional white noise.

Functional data analysis in a mixed model framework
Likelihood based analysis of 1-dimension functional data
in a mixed-effets model framework. Matrix computation are
approximated by semi-explicit operator equivalents with linear
computational complexity.

Likelihood based analysis of 1-dimension functional data
in a mixed-effects model framework. Matrix computation are
approximated by semi-explicit operator equivalents with linear
computational complexity.