visStatistics 0.2.0

Changes in decision logic

• Unified normality testing across all branches. The two-group case (t-test vs Wilcoxon) and the multi-group case (ANOVA vs Kruskal-Wallis) now share the same normality test: shapiro.test() applied to the internally studentized residuals from lm(y ~ x) via rstandard().

• Variance homogeneity test changed from bartlett.test() to levene.test(). The Levene-Brown-Forsythe test (using the median) is more robust to non-normality than Bartlett’s test. The test now determines whether Student’s t-test (t.test(var.equal = TRUE)) or Welch’s t-test is used in the two-group case, and whether aov() or oneway.test() is used in the multi-group case.

• Large-sample threshold changed from 30 to 50 per group. When every group exceeds 50 observations, normality testing is skipped and parametric tests are applied directly (justified by the central limit theorem).

• Post-hoc test selection. When oneway.test() (Welch’s ANOVA) is used for unequal variances, games.howell() is the appropriate post-hoc test, as it does not assume equal variances. TukeyHSD() remains for aov() (Student’s ANOVA).

• Rank correlation (correlation = TRUE). Selects the most appropriate rank correlation for the data type: Spearman’s \(\rho\) for two numeric variables, Kendall’s \(\tau_b\) when both variables are ordered factors. This is the only test decision not made automatically; it requires an explicit user choice.

New features

• Formula interface: visstat(y ~ x, data = df) is now supported alongside the existing visstat(x, y) and visstat(dataframe, "namey", "namex") forms.

• Ordered factor responses: When the response is of class ordered, visstat() converts it to numeric ranks and applies Wilcoxon or Kruskal-Wallis. When both variables are ordered and correlation = TRUE, Kendall’s \(\tau_b\) is used instead.

• Correlation analysis: New parameter correlation in visstat(). When set to TRUE, selects Spearman rank correlation for numeric variables or Kendall’s \(\tau_b\) for two ordered factors, instead of fitting a regression model.

• New exported functions:

  • levene.test(): Levene-Brown-Forsythe test for homogeneity of variance (center = median), mimicking the default behaviour of leveneTest() in the car package.
  • bp.test(): Breusch-Pagan test for heteroscedasticity in linear regression models.
  • games.howell(): Games-Howell post-hoc test for pairwise comparisons following Welch’s ANOVA.
  • vis_numeric(): Visualisation of numeric-numeric relationships (regression or correlation).
  • vis_group_normality(): Diagnostic plots for the Welch t-test / Welch ANOVA branch.
  • vis_lm_assumptions(): Renamed from vis_anova_assumptions(), now provides unified assumption diagnostics for the general linear model (t-test, ANOVA, regression).

Breaking changes

• vis_anova_assumptions() has been removed and replaced by vis_lm_assumptions(). The new function handles both ANOVA and regression diagnostics (controlled by the regression parameter). For regression, it shows a Residuals vs Leverage plot (with Cook’s distance contours) and the Breusch-Pagan test instead of the Bartlett test.

Deprecated

• vis_anova_assumptions() is provided as a deprecated wrapper for vis_lm_assumptions(). It will be removed in a future version.

Improvements

• Diagnostic plot title now reads “Linear model assumptions” with all test p-values on two lines.
• plot.visstat() method added to the visstat class.
• Diagnostic plots for normality now include a histogram overlaid with the normal density curve.
• All assumption plots are saved with the prefix “assumption”.

Documentation

• Vignette substantially revised:

  • Spearman correlation section rewritten, justification for Spearman-only approach added.
  • Complete decision logic rewritten to reflect the new test selection algorithm.
  • The general linear model framework (Appendix A) introduced.
  • Clarified that rstandard() computes internally studentized residuals, with reference to Cook and Weisberg (1982).
  • Bibliography extended.

• DESCRIPTION rewritten to reflect the updated test selection algorithm.

visStatistics 0.1.7

• No user-visible changes relative to 0.1.6.

visStatistics 0.1.6

Major changes

• The visstat() function interface has been updated to accept two vectors directly, enabling a more concise and idiomatic usage. For example:

  visstat(trees\(Girth, trees\)Height)

  yields the same result as the original form:

  visstat(trees, “Height”, “Girth”)

  This change aligns with standard R conventions. Both calling styles remain supported for backwards compatibility.

Structural Improvements

• The visstat() function now returns an object of class "visstat", enabling consistent method dispatch.
• New S3 methods added:
  • print.visstat() – shows a concise summary,
  • summary.visstat() – prints the full test and post hoc summaries.

visStatistics 0.1.5

News

• Extended vignette: all implemented tests are explained in greater detail.
• Graphical output displays the corresponding test statistics, in addition to p-values, where appropriate.
• Internal helper function get_samples_fact_inputfile() no longer exported to NAMESPACE.

Bug fixes

• The legend for the Šidák-corrected confidence interval no longer incorrectly states that it displays group means.

visStatistics 0.1.3

News

• Added vignette visStatistics.Rmd documenting the statistical decision logic, with reproducible examples illustrating each test case.
• Added a graphical summary of the decision logic to the README and vignette.

Improvements

• Extended README.html and the @details section of the main function visstat():
  • More precise parameter descriptions.
  • Clearer presentation of the decision logic.

Change in decision logic

• Welch’s t-test (t.test()) is now applied when both groups have more than 30 observations (previous threshold was 100).

Bug fixes

• Confidence and prediction bands in regression now correctly reflect the specified conf.level rather than defaulting to 0.95.
• Post hoc analysis in the Kruskal–Wallis test (pairwise.wilcox.test()) now uses the specified conf.level.
• Switching to fisher.test() now correctly follows the expected cell count thresholds.