The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

visStatistics: Automated Selection and Visualisation of Statistical Hypothesis Tests

Automatically selects and visualises statistical hypothesis tests between two vectors, based on their class, distribution, sample size, and a user-defined confidence level (conf.level). 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 helps the user focus on the interpretation of test results rather than test selection. It is particularly suited for quick data analysis, e.g., in statistical consulting projects or educational settings. The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical. 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 vector is numerical and the predictor vector is categorical, a test of central tendencies is selected. If the categorical predictor has exactly two levels, t.test() is applied when group sizes exceed 30 (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>). For smaller samples, normality of residuals is tested using shapiro.test(); if met, t.test() is used; otherwise, wilcox.test(). If the predictor is categorical with more than two levels, an aov() is initially fitted. Residual normality is evaluated using both shapiro.test() and ad.test(); residuals are considered approximately normal if at least one test yields a p-value above alpha. If this assumption is met, bartlett.test() assesses variance homogeneity. If variances are homogeneous, aov() is used; otherwise oneway.test(). Both tests are followed by TukeyHSD(). If residual normality cannot be assumed, kruskal.test() is followed by pairwise.wilcox.test(). (2) When both the response and predictor vectors are numerical, a simple linear regression model is fitted using lm(). (3) 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: 0.1.7
Imports: Cairo, graphics, grDevices, grid, multcompView, nortest, stats, utils, vcd
Suggests: knitr, rmarkdown
Published: 2025-05-28
Author: Sabine Schilling ORCID iD [cre, aut, cph] (year: 2025), 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
Materials: README NEWS
CRAN checks: visStatistics results

Documentation:

Reference manual: visStatistics.pdf
Vignettes: visStatistics (source, R code)

Downloads:

Package source: visStatistics_0.1.7.tar.gz
Windows binaries: r-devel: visStatistics_0.1.5.zip, r-release: visStatistics_0.1.5.zip, r-oldrel: visStatistics_0.1.5.zip
macOS binaries: r-release (arm64): visStatistics_0.1.5.tgz, r-oldrel (arm64): visStatistics_0.1.5.tgz, r-release (x86_64): visStatistics_0.1.5.tgz, r-oldrel (x86_64): visStatistics_0.1.5.tgz
Old sources: visStatistics archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=visStatistics to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.