rmarkdown, ANTsR, ITKR, extrantsr, MNITemplate, RAVEL,
neurodata

An R Toolbox for Psychologists, Neuropsychologists and
Neuroscientists
c(person("Dominique", "Makowski", email="dom.makowski@gmail.com", role=c("aut", "cre")))
An R package containing datasets and some statistical functions useful for psychology, neuropsychology and neuroscience.

c(person("Dominique", "Makowski", email="dom.makowski@gmail.com", role=c("aut", "cre")),
person("Phillip", "Alday", email="phillip.alday@unisa.edu.au", role=c("ctb")))
An R package containing free datasets as well as several statistical functions useful in psychology, neuropsychology and neuroscience.
Dominique Makowski [aut, cre],
Phillip Alday [ctb]

Contains free datasets and statistical functions useful in psychology, neuropsychology and neuroscience.

Toolbox for Psychologists, Neuropsychologists and
Neuroscientists
Contains statistical functions (for patient assessment, data preprocessing and reporting, ...) and datasets useful in psychology, neuropsychology and neuroscience.
dplyr, tidyr, ggplot2, lme4, psych, ggcorrplot, Hmisc, MuMIn,
png, tm, wordcloud2, BayesFactor, htmlTable, ppcor, stringi,
caret

Uses 'Neurovault' API <https://neurovault.org/api-docs> to
download statistical maps of the human brain stored on that platform.
knitr, rmarkdown, covr, testthat, neurobase

Test for a Neutral Evolutionary Model in Cancer Sequencing Data
person("Marc", "Williams", email = "marcjwilliams1@gmail.com", role = c("aut", "cre"))
Package takes frequencies of mutations as reported by high throughput sequencing data from cancer and fits a theoretical neutral model of tumour evolution. Package outputs summary statistics and contains code for plotting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> for further details of the method.
dplyr, ggplot2, scales, pracma, ggpmisc, cowplot

c(
person(given = "Ilenia",
family = "Lovato",
role = "aut",
email = "ilenia.lovato01@universitadipavia.it"),
person(given = "Alessia",
family = "Pini",
role = "aut",
email = "alessia.pini@unicatt.it"),
person(given = "Aymeric",
family = "Stamm",
role = c("aut", "cre"),
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"))
A flexible statistical framework for network-valued data analysis.
It leverages the complexity of the space of distributions on graphs by using
the permutation framework for inference as implemented in the 'flipr' package.
Currently, only the two-sample testing problem is covered and generalization
to k samples and regression will be added in the future as well. It is a
4-step procedure where the user chooses a suitable representation of the
networks, a suitable metric to embed the representation into a metric space,
one or more test statistics to target specific aspects of the distributions
to be compared and a formula to compute the permutation p-value. Two types
of inference are provided: a global test answering whether there is a
difference between the distributions that generated the two samples and a
local test for localizing differences on the network structure. The latter
is assumed to be shared by all networks of both samples. References: Lovato,
I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for
network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A.,
Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis
testing for network-valued data: Analysis of brain networks of patients with
autism" <doi:10.1111/rssc.12463>.
igraph, Rcpp, tidyr, dplyr, purrr, tibble, forcats, ggplot2,
rlang, magrittr, flipr, cli, withr
Ilenia Lovato [aut],
Alessia Pini [aut],
Aymeric Stamm [aut, cre] (<https://orcid.org/0000-0002-8725-3654>),
Simone Vantini [aut]