c(
person(given = "Nicholas",
family = "Tarabelloni",
role = "aut",
email = "nicholas.tarabelloni@polimi.it"),
person(given = "Ana",
family = "Arribas-Gil",
role = "aut",
email = "aarribas@est-econ.uc3m.es"),
person(given = "Francesca",
family = "Ieva",
role = "aut",
email = "francesca.ieva@polimi.it"),
person(given = "Anna Maria",
family = "Paganoni",
role = "aut",
email = "anna.paganoni@polimi.it"),
person(given = "Juan",
family = "Romo",
role = "aut",
email = "romo@est-econ.uc3m.es"),
person(given = "Francesco",
family = "Palma",
role = "ctb",
email = "frapalma7892@gmail.com"),
person(given = "Aymeric",
family = "Stamm",
role = c("ctb", "cre"),
email = "aymeric.stamm@math.cnrs.fr",
comment = c(ORCID = "0000-0002-8725-3654")),
person(given = "Antonio",
family = "Elias-Fernandez",
role = "ctb"))
A collection of methods for the robust analysis of univariate and
multivariate functional data, possibly in high-dimensional cases, and hence
with attention to computational efficiency and simplicity of use. See the R
Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an
in-depth presentation of the 'roahd' package. See Aleman-Gomez et al. (2021)
<arXiv:2103.08874> for details about the concept of depthgram.
testthat (>= 3.0.0), knitr, rmarkdown, withr, vdiffr
scales, robustbase, magrittr, dplyr, ggplot2, plotly
Nicholas Tarabelloni [aut],
Ana Arribas-Gil [aut],
Francesca Ieva [aut],
Anna Maria Paganoni [aut],
Juan Romo [aut],
Francesco Palma [ctb],
Aymeric Stamm [ctb, cre] (<https://orcid.org/0000-0002-8725-3654>),
Antonio Elias-Fernandez [ctb]

Robust Estimation for Compositional Data.
utils, e1071, robustbase, compositions, car, MASS
Peter Filzmoser, Karel Hron, Matthias Templ
This first version of the package includes methods for imputation of compositional data including robust methods. The package will be enhanced with other multivariate methods for compositional data in near future.

utils, robustbase, car, e1071, MASS, compositions
Matthias Templ, Karel Hron, Peter Filzmoser
The package includes methods for imputation of
compositional data including robust methods, (robust) outlier
detection for compositional data, (robust) principal component
analysis for compositional data, (robust) factor analysis for
compositional data and (robust) Anderson-Darling normality
tests for compositional data.