smartcor 1.0.1

CRAN resubmission after reviewer feedback. No statistical results change.

• The Description field now cites the accompanying paper: Harshvardhan and Ranjan (2026) doi:10.48550/arXiv.2607.22285.
• The ggcor_heatmap() / ggcor_method_heatmap() example is no longer wrapped in \dontrun{}; it uses only bundled data and runs during checks.
• The internal seed helper no longer touches the global environment directly; it delegates to 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”.

smartcor 1.0.0

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.
• Heatmap visualizations: ggcor_heatmap(), ggcor_method_heatmap(), and plot_cor_heatmap().
• tidy() method for pipeline-friendly output.

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