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 |