visStatistics: Automated Selection and Visualisation of Statistical Hypothesis
Tests
The right test, visualised. 'visStatistics' automatically
selects and visualises statistical hypothesis tests comparing two vectors,
based on their class, distribution, and sample size.
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. If every group contains more than
50 observations, the sampling distribution of the group means is assumed
approximately normal by the central limit theorem (Lumley et al. (2002)
<doi:10.1146/annurev.publhealth.23.100901.140546>); otherwise, residual
normality is assessed 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
pairwise.wilcox.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.
(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().
| Version: |
0.2.0 |
| Imports: |
Cairo, graphics, grDevices, grid, multcompView, nortest, stats, utils, vcd |
| Suggests: |
knitr, rmarkdown, spelling, testthat (≥ 3.0.0) |
| Published: |
2026-05-12 |
| DOI: |
10.32614/CRAN.package.visStatistics |
| Author: |
Sabine Schilling 
[cre, aut, cph] (year: 2026),
Peter Kauf [ctb] |
| Maintainer: |
Sabine Schilling <sabineschilling at gmx.ch> |
| BugReports: |
https://github.com/shhschilling/visStatistics/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/shhschilling/visStatistics,
https://shhschilling.github.io/visStatistics/ |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README, NEWS |
| In views: |
TeachingStatistics |
| CRAN checks: |
visStatistics results |
Documentation:
Downloads:
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