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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=unusualprofile to link to this page.
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