Package: nevada
Type: Package
Title: Network-Valued Data Analysis
Version: 0.2.0.9000
Authors@R: 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@cnrs.fr",
           comment = c(ORCID = "0000-0002-8725-3654")), 
    person(given = "Simone", 
           family = "Vantini", 
           role = "aut", 
           email = "simone.vantini@polimi.it"))
Description: 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>.
License: GPL (>= 3)
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: igraph, Rcpp, tidyr, dplyr, purrr, tibble, forcats, ggplot2,
        rlang, flipr, cli, withr, tsne, umap, furrr, rgeomstats,
        tidygraph
RoxygenNote: 7.3.2
Roxygen: list(markdown = TRUE)
URL: https://astamm.github.io/nevada/,
        https://github.com/astamm/nevada/
BugReports: https://github.com/astamm/nevada/issues/
Suggests: knitr, rmarkdown, covr, quarto
VignetteBuilder: quarto
LinkingTo: Rcpp, RcppArmadillo
Config/pak/sysreqs: cmake git libglpk-dev make libgit2-dev libicu-dev
        libpng-dev libuv1-dev libxml2-dev libssl-dev python3 libx11-dev
Repository: https://permaverse.r-universe.dev
Date/Publication: 2024-12-11 17:13:40 UTC
RemoteUrl: https://github.com/permaverse/nevada
RemoteRef: HEAD
RemoteSha: 109dbc64f2903d41b1159e8b4b1d4d271a9a633c
NeedsCompilation: yes
Packaged: 2026-07-12 08:42:39 UTC; root
Author: Ilenia Lovato [aut],
  Alessia Pini [aut],
  Aymeric Stamm [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-8725-3654>),
  Simone Vantini [aut]
Maintainer: Aymeric Stamm <aymeric.stamm@cnrs.fr>