Package: smartcor 1.0.1

smartcor: Automatic Correlation Method Selection Based on Variable Types

Detects variable types (continuous, count, binary, ordinal, categorical) and selects the appropriate correlation method for each pair. Supports Pearson, Spearman, Kendall's tau, point-biserial, rank-biserial, phi, tetrachoric, polychoric, polyserial, Cramer's V, Tschuprow's T, Theil's U, Yule's Q, and Goodman-Kruskal's gamma, each with a confidence interval and p-value. Explains the selection rationale in the output, follows tidy data principles, and works in both interactive and scripted workflows. The methodology is described in Harshvardhan and Ranjan (2026) <doi:10.48550/arXiv.2607.22285>.

Authors:M. Harshvardhan [aut, cre, cph], Pritam Ranjan [aut, cph]

smartcor_1.0.1.tar.gz
smartcor_1.0.1.zip(r-4.7-any)smartcor_1.0.1.zip(r-4.6-any)smartcor_1.0.1.zip(r-4.5-any)
smartcor_1.0.1.tgz(r-4.6-any)smartcor_1.0.1.tgz(r-4.5-any)
smartcor_1.0.1.tar.gz(r-4.7-any)smartcor_1.0.1.tar.gz(r-4.6-any)
smartcor_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
smartcor/json (API)

# Install 'smartcor' in R:
install.packages('smartcor', repos = c('https://harshvardhaniimi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/harshvardhaniimi/smartcor/issues

Pkgdown/docs site:https://harshvardhaniimi.github.io

On CRAN:

Conda:

4.83 score 15 scripts 243 downloads 11 exports 28 dependencies

Last updated from:fd401729cf. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK231
source / vignettesOK206
linux-releaseOK225
macos-releaseOK239
macos-oldrelOK173
windows-develOK188
windows-releaseOK208
windows-oldrelOK179
wasm-releaseOK99

Exports:available_methodscompare_methodsdetect_typeggcor_heatmapggcor_method_heatmapplot_cor_heatmapsmart_corsmart_cor_dfsmart_cormattest_bivariate_normalitytidy

Dependencies:admiscclicpp11farvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixmvtnormpillarpkgconfigpolycorR6RColorBrewerrlangS7scalestibbleutf8vctrsviridisLitewithr

Getting Started with smartcor
Why smartcor? | Installation | Quick Start | Pairwise Correlation | Ordinal Variables | Categorical Variables | Override Type Detection | Force a Specific Method | Correlation Matrix | Tidy Output | Available Methods | Method Selection Logic | The Latent-Normality Decision | Dependencies
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-08-17
Started: 2026-07-24

Choosing the Right Correlation Method: Theory and Rationale
Introduction | Variable Types | Correlation Methods by Variable-Type Combination | Continuous + Continuous: Pearson Correlation | Continuous + Binary: Point-Biserial Correlation | Binary + Binary: Phi Coefficient | Binary + Binary (Latent Normal): Tetrachoric Correlation | Ordinal + Ordinal: Polychoric Correlation | Continuous + Ordinal: Polyserial Correlation | Binary + Ordinal: Rank-Biserial Correlation | Categorical + Categorical: Cramer's V | Other Combinations | Why Not Always Use Pearson? | The Latent Normality Question | Summary of Method Selection Logic | References
Rendered fromtheory.Rmdusingknitr::rmarkdown

Last update: 2026-08-04
Started: 2026-07-24

Confidence intervals and p-values
Every estimate ships with its uncertainty | The inference payload | Analytic intervals are the default | The bootstrap argument | The null hypothesis is method-specific | Reading interval widths | Summary
Rendered frominference.Rmdusingknitr::rmarkdown

Last update: 2026-07-24
Started: 2026-07-24