Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
| Version: | 1.2.0 |
| Depends: | R (≥ 3.2.0) |
| Imports: | shiny, visNetwork, infotheo, minerva, energy, mclust, rAmCharts, Hmisc, pbapply, ggplot2, dplyr, tidyr |
| Suggests: | knitr, rmarkdown |
| Published: | 2018-07-22 |
| Author: | Alassane Samba [aut, cre], Orange [cph] |
| Maintainer: | Alassane Samba <alassane.samba at orange.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | linkspotter results |
| Reference manual: | linkspotter.pdf |
| Vignettes: |
Vignette Title |
| Package source: | linkspotter_1.2.0.tar.gz |
| Windows binaries: | r-devel: linkspotter_1.2.0.zip, r-release: linkspotter_1.2.0.zip, r-oldrel: linkspotter_1.2.0.zip |
| OS X binaries: | r-release: linkspotter_1.2.0.tgz, r-oldrel: linkspotter_1.2.0.tgz |
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