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CRAN resubmission after reviewer feedback. No statistical results change.
ggcor_heatmap() /
ggcor_method_heatmap() example is no longer wrapped in
\dontrun{}; it uses only bundled data and runs during
checks.withr::with_seed() (withr added
to Imports). Seeded results are unchanged, and a seeded call still
restores the caller’s .Random.seed.graphics and utils are now declared in
Imports (both were already imported in NAMESPACE).inst/CITATION, the README, and the documentation now
cite the published arXiv preprint (arXiv:2607.22285) instead of “work in
progress”.First public release.
smart_cor() detects the types of two variables and
selects an appropriate correlation method (Pearson, Spearman, Kendall’s
tau-b, polychoric, polyserial, tetrachoric, point-biserial,
rank-biserial, Goodman-Kruskal gamma, or phi), with analytic inference
on the Fisher-z scale and optional bootstrap.smart_cormat() and smart_cor_df() extend
automatic selection to full mixed-type data frames and correlation
matrices.compare_methods() reports all applicable estimators for
a pair side by side.test_bivariate_normality() implements the
likelihood-ratio chi-square test for bivariate normality (Joreskog
2005); smart_cor() runs it automatically before reporting
polychoric/polyserial estimates and falls back to Kendall’s tau-b when
it rejects.detect_type() and available_methods()
expose the type-detection and method-lookup machinery.ggcor_heatmap(),
ggcor_method_heatmap(), and
plot_cor_heatmap().tidy() method for pipeline-friendly output.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.
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