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.