R (>= 3.5.0), stats, grDevices, graphics, geometry, rgl,
Matrix, plot3D, methods

Partially Observed Integrated Functional Depth
c(person("Antonio", "Elías", email = "antonioefz91@gmail.com", role = c("aut", "cre")),
person("Raúl", "Jiménez", email = "", role = c("ctb")),
person("Anna M.", "Paganoni", email = "", role = c("ctb")),
person("Laura M.", "Sangalli", email = "", role = c("ctb")))
Integrated Depths for Partially Observed Functional Data (PoFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".
ggplot2, tibble, magrittr, reshape2, patchwork, MASS, fdapace,
FastGP, stats
Antonio Elías [aut, cre],
Raúl Jiménez [ctb],
Anna M. Paganoni [ctb],
Laura M. Sangalli [ctb]

Integrated Depths for Partially Observed Functional Data (POFD). Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2020), "Integrated Depth for Partially Observed Functional Data".

Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2022), "Integrated Depth for Partially Observed Functional Data". Journal of Computational and Graphical Statistics. <doi:10.1080/10618600.2022.2070171>.

Joint Clustering and Alignment of Functional Data
c(
person(given = "Laura",
family = "Sangalli",
role = "aut",
email = "laura.sangalli@polimi.it"),
person(given = "Piercesare",
family = "Secchi",
role = "aut",
email = "piercesare.secchi@polimi.it"),
person(given = "Aymeric",
family = "Stamm",
role = c("cre", "ctb"),
email = "aymeric.stamm@math.cnrs.fr",
comment = c(ORCID = "0000-0002-8725-3654")),
person(given = "Simone",
family = "Vantini",
role = "aut",
email = "simone.vantini@polimi.it"),
person(given = "Valeria",
family = "Vitelli",
role = "aut",
email = "valeria.vitelli@medisin.uio.no"),
person(given = "Alessandro",
family = "Zito",
role = "ctb",
email = "zito.ales@gmail.com"))
Revisited clustering approaches to accommodate functional
data by allowing to jointly align the data during the clustering
process. Currently, shift, dilation and affine transformations
only are available to perform alignment. The k-mean algorithm has
been extended to integrate alignment and is fully parallelized.
Hierarchical clustering will soon be available as well.
Rcpp, magrittr, tibble, dplyr, tidyr, purrr, ggplot2, nloptr
Laura Sangalli [aut],
Piercesare Secchi [aut],
Aymeric Stamm [cre, ctb] (<https://orcid.org/0000-0002-8725-3654>),
Simone Vantini [aut],
Valeria Vitelli [aut],
Alessandro Zito [ctb]