expm, fda.usc, goffda, mvtnorm, pcaPP, plot3D
c(
person("Ufuk", "Beyaztas", email="ufukbeyaztas@gmail.com", role=c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-5208-4950")),
person("Han Lin", "Shang", role="aut", comment = c(ORCID = "0000-0003-1769-6430"))
)
Functions for implementing robust methods for functional linear regression. In the functional linear regression, we consider scalar-on-function linear regression and function-on-function linear regression. More details, see Beyaztas, U., and Shang, H. L. (2021) <arXiv:2111.01238> and Beyaztas, U., and Shang, H. L. (2022) <arXiv:2203.05065>.

c(
person("Valeria", "Policastro", role=c("aut", "cre"), email="valeria.policastro@gmail.com"),
person("Dario", "Righelli", role=c("aut"), email="dario.righelli@gmail.com"),
person("Luisa", "Cutillo", role=c("aut"), email="l.cutillo@leeds.ac.uk"),
person("Italia", "De Feis", role=c("aut"), email="i.defeis@na.iac.cnr.it"),
person("Annamaria", "Carissimo", role=c("aut"), email="a.carissimo@na.iac.cnr.it"))
Many community detection algorithms have been developed in network
analysis. However, their applications leave unaddressed the statistical
validation of the results, for this reason we developed ROBIN (ROBustness
In Network), a useful method for the validation of community detection.
It has a double aim, it studies the robustness of a single community
detection algorithm and compares two community detection algorithms to
understand which provides the best partition. Reference in Annamaria Carissimo,
Luisa Cutillo, Italia De Feis (2018) <doi:10.1016/j.csda.2017.10.006>.
ggplot2, networkD3, DescTools, fdatest, gridExtra, methods
devtools, cowplot, knitr, rmarkdown, testthat (>= 2.1.0)
Valeria Policastro [aut, cre],
Dario Righelli [aut],
Luisa Cutillo [aut],
Italia De Feis [aut],
Annamaria Carissimo [aut]

ggplot2, networkD3, DescTools, fdatest, methods

person("Valeria", "Policastro", role=c("aut", "cre"), email="valeria.policastro@gmail.com")
In network analysis, many community detection algorithms have been developed. However, their applications leave unaddressed one important question: the statistical validation of the results. robin (ROBustness In Network) is an R package for the validation of community detection. It has a double aim: to study the robustness of a community detection algorithm and to compare the robustness of different community detection algorithms on the same network. Reference in Annamaria Carissimo, Luisa Cutillo, Italia De Feis (2018) <doi:10.1016/j.csda.2017.10.006>.