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  "Package": "visStatistics",
  "Title": "Automated Selection and Visualisation of Statistical Hypothesis\nTests",
  "Version": "0.3.0",
  "Authors@R": "c(\nperson(\"Sabine\", \"Schilling\", , \"sabineschilling@gmx.ch\", role = c(\"cre\", \"aut\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-8318-9421\", year = \"2026\")),\nperson(\"Peter\", \"Kauf\", , \"peter.kauf@zhaw.ch\", role = \"ctb\")\n)",
  "Author": "Sabine Schilling [cre, aut, cph] (ORCID:\n<https://orcid.org/0000-0002-8318-9421>, year: 2026), Peter\nKauf [ctb]",
  "Maintainer": "Sabine Schilling <sabineschilling@gmx.ch>",
  "Description": "Automated test selection, visualised. 'visStatistics'\nautomatically selects and visualises statistical hypothesis\ntests comparing two vectors, based on their class and\ndistribution. Visual outputs, including box plots, bar charts,\nregression lines with confidence bands, mosaic plots, residual\nplots, and Q-Q plots, are annotated with relevant test\nstatistics, assumption checks, and post-hoc analyses where\napplicable.  The algorithmic workflow shifts attention from\nad-hoc test selection to visual diagnostic assessment and\nstatistical interpretation. It is particularly suited for\nserver-side R applications, where end users interact solely\nthrough a web interface to select data groups and receive a\ncomplete visual statistical analysis automatically. The same\nautomation makes it useful in time-constrained contexts such as\nstatistical consulting, where it reduces effort spent on test\nselection and leaves more room for interpretation.  The\nimplemented tests cover the most frequently applied inferential\nmethods in biomedical research (Hayat et al. (2017)\n<doi:10.1371/journal.pone.0179032>).  The test selection\nalgorithm proceeds as follows: Input vectors of class numeric\nor integer are considered numerical; those of class factor are\nconsidered categorical; those of class ordered are considered\nordinal. Assumptions of residual normality and homogeneity of\nvariances are considered met if the corresponding test yields a\np-value greater than the significance level alpha = 1 -\nconf.level.  (1) When the response is numerical and the\npredictor is categorical, a test comparing central tendencies\nis selected. In the default setting (group_test = NULL),\nresidual normality is assessed at every group size using\nshapiro.test() applied to the standardised residuals of lm().\nIf normality is not met, wilcox.test() is used when the\npredictor has two levels and kruskal.test() followed by\ndunn.test() otherwise. If normality is met, levene.test()\nassesses variance homogeneity. For two-level predictors,\nStudent's t.test(var.equal = TRUE) is applied if variances are\nhomogeneous and Welch's t.test() otherwise. For predictors with\nmore than two levels, aov() followed by TukeyHSD() is applied\nif variances are homogeneous, and oneway.test() followed by\ngames.howell() otherwise. Setting group_test to \"welch\" or\n\"rank\" bypasses these assumption tests and fixes the analysis\nto Welch-type or to rank-based tests, respectively. (2) When\nboth vectors are numerical, lm() is fitted by default\n(correlation = FALSE). If correlation = TRUE, Spearman rank\ncorrelation is performed.  (3) When the response is ordinal, it\nis converted to numeric ranks and the non-parametric path from\n(1) is followed (Wilcoxon or Kruskal-Wallis). When both\nvariables are ordinal and correlation = TRUE, Kendall's tau_b\nis used instead.  (4) When both vectors are categorical,\nCochran's rule (Cochran (1954) <doi:10.2307/3001666>) is\napplied to test independence either by chisq.test() or\nfisher.test().",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/shhschilling/visStatistics,\nhttps://shhschilling.github.io/visStatistics/",
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  "Date/Publication": "2026-09-28 17:57:06 UTC",
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