Type: Package
Package: visStatistics
Title: Automated Selection and Visualisation of Statistical Hypothesis
        Tests
Version: 0.3.0
Authors@R: c(
    person("Sabine", "Schilling", , "sabineschilling@gmx.ch", role = c("cre", "aut", "cph"),
           comment = c(ORCID = "0000-0002-8318-9421", year = "2026")),
    person("Peter", "Kauf", , "peter.kauf@zhaw.ch", role = "ctb")
  )
Author: Sabine Schilling [cre, aut, cph] (ORCID:
        <https://orcid.org/0000-0002-8318-9421>, year: 2026), Peter
        Kauf [ctb]
Maintainer: Sabine Schilling <sabineschilling@gmx.ch>
Description: Automated test selection, visualised. 'visStatistics'
        automatically selects and visualises statistical hypothesis
        tests comparing two vectors, based on their class and
        distribution. Visual outputs, including box plots, bar charts,
        regression lines with confidence bands, mosaic plots, residual
        plots, and Q-Q plots, are annotated with relevant test
        statistics, assumption checks, and post-hoc analyses where
        applicable.  The algorithmic workflow shifts attention from
        ad-hoc test selection to visual diagnostic assessment and
        statistical interpretation. It is particularly suited for
        server-side R applications, where end users interact solely
        through a web interface to select data groups and receive a
        complete visual statistical analysis automatically. The same
        automation makes it useful in time-constrained contexts such as
        statistical consulting, where it reduces effort spent on test
        selection and leaves more room for interpretation.  The
        implemented tests cover the most frequently applied inferential
        methods in biomedical research (Hayat et al. (2017)
        <doi:10.1371/journal.pone.0179032>).  The test selection
        algorithm proceeds as follows: Input vectors of class numeric
        or integer are considered numerical; those of class factor are
        considered categorical; those of class ordered are considered
        ordinal. Assumptions of residual normality and homogeneity of
        variances are considered met if the corresponding test yields a
        p-value greater than the significance level alpha = 1 -
        conf.level.  (1) When the response is numerical and the
        predictor is categorical, a test comparing central tendencies
        is selected. In the default setting (group_test = NULL),
        residual normality is assessed at every group size using
        shapiro.test() applied to the standardised residuals of lm().
        If normality is not met, wilcox.test() is used when the
        predictor has two levels and kruskal.test() followed by
        dunn.test() otherwise. If normality is met, levene.test()
        assesses variance homogeneity. For two-level predictors,
        Student's t.test(var.equal = TRUE) is applied if variances are
        homogeneous and Welch's t.test() otherwise. For predictors with
        more than two levels, aov() followed by TukeyHSD() is applied
        if variances are homogeneous, and oneway.test() followed by
        games.howell() otherwise. Setting group_test to "welch" or
        "rank" bypasses these assumption tests and fixes the analysis
        to Welch-type or to rank-based tests, respectively. (2) When
        both vectors are numerical, lm() is fitted by default
        (correlation = FALSE). If correlation = TRUE, Spearman rank
        correlation is performed.  (3) When the response is ordinal, it
        is converted to numeric ranks and the non-parametric path from
        (1) is followed (Wilcoxon or Kruskal-Wallis). When both
        variables are ordinal and correlation = TRUE, Kendall's tau_b
        is used instead.  (4) When both vectors are categorical,
        Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is
        applied to test independence either by chisq.test() or
        fisher.test().
License: MIT + file LICENSE
URL: https://github.com/shhschilling/visStatistics,
        https://shhschilling.github.io/visStatistics/
BugReports: https://github.com/shhschilling/visStatistics/issues
Imports: Cairo, graphics, grDevices, grid, multcompView, nortest,
        stats, tools, utils, vcd
Suggests: bookdown, knitr, rmarkdown, spelling, testthat (>= 3.0.0)
VignetteBuilder: knitr
BuildVignettes: true
Config/roxygen2/version: 8.0.0
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-GB
NeedsCompilation: no
Config/pak/sysreqs: libcairo2-dev
Repository: https://shhschilling.r-universe.dev
Date/Publication: 2026-09-28 17:57:06 UTC
RemoteUrl: https://github.com/shhschilling/visstatistics
RemoteRef: HEAD
RemoteSha: 739089c251d7d32cb5b3fd75391fa200b92070b8
Packaged: 2026-09-28 22:53:28 UTC; root
Built: R 4.6.0; ; 2026-09-28 22:56:22 UTC; unix
