{ "_id": "6a1ef2deb401979e73414cfe", "Type": "Package", "Package": "visStatistics", "Title": "Automated Selection and Visualisation of Statistical Hypothesis\nTests", "Version": "0.2.1", "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, year: 2026), Peter\nKauf [ctb]", "Maintainer": "Sabine Schilling ", "Description": "Automated test selection, visualised. 'visStatistics'\nautomatically selects and visualises statistical hypothesis\ntests comparing two vectors, based on their class,\ndistribution, and sample size. Visual outputs, including box\nplots, bar charts, regression lines with confidence bands,\nmosaic plots, residual plots, and Q-Q plots, are annotated with\nrelevant test statistics, assumption checks, and post-hoc\nanalyses where applicable. The algorithmic workflow shifts\nattention from ad-hoc test selection to visual diagnostic\nassessment and statistical interpretation. It is particularly\nsuited for server-side R applications, where end users interact\nsolely through a web interface to select data groups and\nreceive a complete visual statistical analysis automatically.\nThe same automation makes it useful in time-constrained\ncontexts such as statistical consulting, where it reduces\neffort spent on test selection and leaves more room for\ninterpretation. The implemented tests cover the most\nfrequently applied inferential methods in biomedical research\n(Hayat et al. (2017) ). The\ntest selection algorithm proceeds as follows: Input vectors of\nclass numeric or integer are considered numerical; those of\nclass factor are considered categorical; those of class ordered\nare considered ordinal. Assumptions of residual normality and\nhomogeneity of variances are considered met if the\ncorresponding test yields a p-value greater than the\nsignificance level alpha = 1 - conf.level. (1) When the\nresponse is numerical and the predictor is categorical, a test\ncomparing central tendencies is selected. If every group\ncontains more than 50 observations, the sampling distribution\nof the group means is assumed approximately normal by the\ncentral limit theorem (Lumley et al. (2002)\n); otherwise,\nresidual normality is assessed using shapiro.test() applied to\nthe standardised residuals of lm(). If normality is not met,\nwilcox.test() is used when the predictor has two levels and\nkruskal.test() followed by pairwise.wilcox.test() otherwise. If\nnormality is met, levene.test() assesses variance homogeneity.\nFor two-level predictors, Student's t.test(var.equal = TRUE) is\napplied if variances are homogeneous and Welch's t.test()\notherwise. For predictors with more than two levels, aov()\nfollowed by TukeyHSD() is applied if variances are homogeneous,\nand oneway.test() followed by games.howell() otherwise. (2)\nWhen both 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) ) 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/", "BugReports": "https://github.com/shhschilling/visStatistics/issues", "VignetteBuilder": "knitr", "BuildVignettes": "true", "Config/roxygen2/version": "8.0.0", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-GB", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 15:06:38 UTC", "User": "root" }, "Config/pak/sysreqs": "libcairo2-dev", "Repository": "https://shhschilling.r-universe.dev", "Date/Publication": "2026-06-02 14:39:16 UTC", "RemoteUrl": "https://github.com/shhschilling/visstatistics", "RemoteRef": "HEAD", "RemoteSha": "12f486a62ad1c1819f18bb5edb82bce5a3f5c768", "MD5sum": "ed8b093732b84b59f907461e83c4de12", "_user": "shhschilling", "_type": "src", "_file": 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The data frame must contain a column with frequencies (counts) as generated by as.data.frame from a contingency table", "topics": [ "counts_to_cases" ] }, { "page": "effect_size", "title": "Compute an effect-size estimate for a visstat result", "topics": [ "effect_size" ] }, { "page": "openGraphCairo", "title": "Cairo wrapper function with plot capture capability", "topics": [ "openGraphCairo" ] }, { "page": "saveGraphVisstat", "title": "Saves Graphical Output with plot capture capability", "topics": [ "saveGraphVisstat" ] }, { "page": "visstat", "title": "Wrapper for visstat_core Allowing Three Different Input Styles", "topics": [ "visstat" ] } ], "_readme": "https://github.com/shhschilling/visstatistics/raw/HEAD/README.md", "_rundeps": [ "Cairo", "colorspace", "lattice", "lmtest", "MASS", "multcompView", "nortest", "vcd", "zoo" ], "_vignettes": [ { "source": "visStatistics.Rmd", "filename": "visStatistics.html", "title": "visStatistics: automated test selection, visualised", "author": "Sabine Schilling", "engine": "knitr::rmarkdown", "headings": [ "Abstract", "Introduction", "Purpose of the vignette", "Package overview", "Decision logic", "Top-level routing by input class", "General linear model framework", "Model definition", "Residuals", "Standardised residuals", "General linear model assumption tests", "Visualisation of the assumptions of the general linear model", "Route-specific decision rules", "Route 1: Numeric response, categorical predictor", "Post-hoc tests", "Route 2: Ordered response", "Route 3: Numeric response, numeric predictor", "Route 4: Two unordered factors", "Optional rank-correlation mode", "Reported p-values and effect sizes", "The visstat methods", "Saved graphics", "Usage and Examples", "Route 1: Numeric response, categorical predictor", "Student's t-test and Fisher's one-way ANOVA", "Student's t-test", "Welch's t-test and Welch's one-way ANOVA", "Welch's t-test", "Wilcoxon rank-sum test and Kruskal--Wallis test", "Wilcoxon rank-sum test", "Route 2: Ordered response", "Ordered response, categorical factor", "Wilcoxon rank-sum test with ordered response", "Kruskal--Wallis test with ordered response", "Route 3: Numeric response, numeric predictor", "Linear regression", "Model exploration outside visstat()", "Plotting the data with Gamma model overlay", "Generate predictions for the overlay", "Route 4: Two unordered factors", "Pearson's $\\chi^2$ test", "Fisher's exact test", "Spearman rank correlation with correlation = TRUE", "Discussion", "Limitations", "Conclusion", "References" ], "created": "2021-09-03 11:03:51", "modified": "2026-06-02 14:24:22", "commits": 167 } ], "_score": 5.155336037465062, "_indexed": true, "_nocasepkg": "visstatistics", "_universes": [ "shhschilling" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.2.1", "date": "2026-06-02T15:08:50.000Z", "distro": "noble", "commit": "12f486a62ad1c1819f18bb5edb82bce5a3f5c768", "fileid": 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