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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "ptestR" in publications use:'
type: software
license: MIT
title: 'ptestR: Permutation-Based Significance Testing for Regression Models'
version: 0.1.1
abstract: Wraps glm(), lme4::lmer(), and binomial glm() with a permutation loop to
  compute nonparametric p-values. For each model, ptestR generates a null distribution
  of the test statistic by randomly rearranging the outcome variable, then computes
  p.perm as the proportion of permuted statistics at least as extreme as the observed
  one. This approach requires far fewer distributional assumptions than standard Wald
  or likelihood-ratio tests, making it well-suited to neuroimaging, EEG, and other
  biomedical datasets with repeated measures and small samples.
authors:
- family-names: França
  given-names: Lucas G. S.
  email: lucas.franca@kcl.ac.uk
  orcid: https://orcid.org/0000-0003-0853-1319
- family-names: Ge
  given-names: Yan
  email: yan.ge@kcl.ac.uk
- family-names: Batalle
  given-names: Dafnis
  email: dafnis.batalle@kcl.ac.uk
  orcid: https://orcid.org/0000-0003-2097-979X
repository: https://code-neuro.r-universe.dev
repository-code: https://github.com/CoDe-Neuro/ptestR
commit: af7815cbc60aa39f3af6bac8576e0e86d79331b1
url: https://code-neuro.github.io/ptestR
date-released: '2026-05-31'
contact:
- family-names: França
  given-names: Lucas G. S.
  email: lucas.franca@kcl.ac.uk
  orcid: https://orcid.org/0000-0003-0853-1319