A flexible permutation framework for making
inference such as point estimation, confidence
intervals or hypothesis testing, on any kind of data,
be it univariate, multivariate, or more complex such
as network-valued data, topological data, functional
data or density-valued data.
purrr, rlang, magrittr, cli, ggplot2, tibble, withr,
viridisLite
rmarkdown, knitr, covr, tidyverse, plotly, htmlwidgets,
htmltools, pracma, usethis

rmarkdown, knitr, covr, tidyverse, plotly, htmlwidgets,
htmltools, pracma

c(
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"),
person(given = "Juliette",
family = "Chiapello",
role = "ctb"))
purrr, rlang, magrittr, cli, ggplot2, tibble, withr,
viridisLite, Rcpp, R6, pbapply, dials, usethis, optimParallel,
rgenoud
rmarkdown, knitr, covr, tidyverse, plotly, htmlwidgets,
htmltools, testthat (>= 3.0.0), akima
Alessia Pini [aut],
Aymeric Stamm [aut, cre] (<https://orcid.org/0000-0002-8725-3654>),
Simone Vantini [aut],
Juliette Chiapello [ctb]

rmarkdown, knitr, covr, tidyverse, plotly, htmlwidgets,
htmltools, testthat (>= 3.0.0), interp

Jesse Hemerik, Jelle Goeman and Livio Finos
Provides two robust tests for testing in GLMs,
by sign-flipping score contributions.
The tests are often robust against overdispersion,
heteroscedasticity and, in some cases,
ignored nuisance variables.
See Hemerik and Goeman (2017) <doi:10.1007/s11749-017-0571-1>.

Provides robust tests for testing in GLMs,
by sign-flipping score contributions.
The tests are often robust against overdispersion,
heteroscedasticity and, in some cases,
ignored nuisance variables.
See Hemerik and Goeman (2017) <doi:10.1007/s11749-017-0571-1>.

Robust Score Testing in GLMs, by Sign-Flip Contributions
Livio Finos, Jesse Hemerik and Jelle Goeman with contribution by Vittorio Giatti
Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables. See Hemerik, Goeman and Finos (2020) <doi:10.1111/rssb.12369>.