Calculates a Mahalanobis distance for every row of a set of outcome variables (Mahalanobis, 1936 <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis distance is calculated using a conditional covariance matrix (i.e., a covariance matrix of the outcome variables after controlling for a set of predictors). Plotting the output of the cond_maha() function can help identify which elements of a profile are unusual after controlling for the predictors.
| Version: | 0.1.4 |
| Depends: | R (≥ 3.1) |
| Imports: | dplyr,ggnormalviolin,ggplot2,magrittr,purrr,rlang, stats,tibble,tidyr |
| Suggests: | bookdown,covr,extrafont,forcats,glue,kableExtra,knitr,lavaan,lifecycle,mvtnorm,patchwork,ragg,rmarkdown,roxygen2,scales,simstandard (≥ 0.6.3),stringr,sysfonts,testthat |
| Published: | 2024-02-14 |
| DOI: | 10.32614/CRAN.package.unusualprofile |
| Author: | W. Joel Schneider [aut, cre], Feng Ji [aut] |
| Maintainer: | W. Joel Schneider <w.joel.schneider at gmail.com> |
| BugReports: | https://github.com/wjschne/unusualprofile/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/wjschne/unusualprofile,https://wjschne.github.io/unusualprofile/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README,NEWS |
| CRAN checks: | unusualprofile results |