{ "_id": "6a467e596ff6f2357554b5fa", "Package": "ptestR", "Title": "Permutation-Based Significance Testing for Regression Models", "Version": "0.1.1", "Authors@R": "c(person(given = \"Lucas G. S.\",\nfamily = \"França\",\nrole = c(\"aut\", \"cre\"),\nemail = \"lucas.franca@kcl.ac.uk\",\ncomment = c(ORCID = \"0000-0003-0853-1319\")),\nperson(given = \"Yan\",\nfamily = \"Ge\",\nrole = \"aut\",\nemail = \"yan.ge@kcl.ac.uk\"),\nperson(given = \"Dafnis\",\nfamily = \"Batalle\",\nrole = \"aut\",\nemail = \"dafnis.batalle@kcl.ac.uk\",\ncomment = c(ORCID = \"0000-0003-2097-979X\")))", "Description": "Wraps glm(), lme4::lmer(), and binomial glm() with a\npermutation loop to compute nonparametric p-values. For each\nmodel, ptestR generates a null distribution of the test\nstatistic by randomly rearranging the outcome variable, then\ncomputes p.perm as the proportion of permuted statistics at\nleast as extreme as the observed one. This approach requires\nfar fewer distributional assumptions than standard Wald or\nlikelihood-ratio tests, making it well-suited to neuroimaging,\nEEG, and other biomedical datasets with repeated measures and\nsmall samples.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "URL": "https://github.com/CoDe-Neuro/ptestR,\nhttps://code-neuro.github.io/ptestR", "BugReports": "https://github.com/CoDe-Neuro/ptestR/issues", "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://code-neuro.r-universe.dev", "Date/Publication": "2026-05-31 13:24:36 UTC", "RemoteUrl": "https://github.com/CoDe-Neuro/ptestR", "RemoteRef": "v0.1.0", "RemoteSha": "af7815cbc60aa39f3af6bac8576e0e86d79331b1", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-02 15:02:22 UTC", "User": "root" }, "Author": "Lucas G. S. 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