Statistics for Holland's Theory of Vocational Choice
R (>= 3.5.0), stats, utils, MplusAutomation
c(person("Joerg-Henrik", "Heine", role = c("aut","cre","com"),
email = "jhheine@googlemail.com"),
person("Florian","Hartmann", role=c("rev", "com"),
email = "florian.hartmann@unibw.de"),
person("Bernhard","Ertl", role=c("rev", "com"),
email = "florian.hartmann@unibw.de"),
person("Christian","Tarnai", role=c("com", "ths"),
email = "christian.tarnai@unibw.de"))
Offers a convenient way to compute parameters
in the framework of the theory of vocational choice introduced by
J.L. Holland, (1997). A comprehensive summary to this theory
of vocational choice is given in Holland, J.L. (1997). Making vocational
choices. A theory of vocational personalities and work environments.
Lutz, FL: Psychological Assessment.
Joerg-Henrik Heine [aut, cre, com],
Florian Hartmann [rev, com],
Bernhard Ertl [rev, com],
Christian Tarnai [com, ths]

R (>= 3.5.0), stats, utils, MplusAutomation, mvtnorm

c(person("Joerg-Henrik", "Heine", role = c("aut","cre","com"),
email = "jhheine@googlemail.com"),
person("Florian","Hartmann", role=c("aut", "com"),
email = "florian.hartmann@unibw.de"),
person("Bernhard","Ertl", role=c("rev", "com"),
email = "florian.hartmann@unibw.de"),
person("Christian","Tarnai", role=c("com", "ths"),
email = "christian.tarnai@unibw.de"))
Joerg-Henrik Heine [aut, cre, com],
Florian Hartmann [aut, com],
Bernhard Ertl [rev, com],
Christian Tarnai [com, ths]

A Tidy Interface for Simulating Multivariate Data
person(given = "Eric",
family = "Scott",
role = c("aut", "cre"),
email = "scottericr@gmail.com")
Provides pipe-friendly (%>%) functions to create simulated multivariate data sets
with groups of variables with different degrees of variance, covariance, and effect size.
dplyr, tibble, MASS, purrr, rlang, assertthat
testthat, covr, knitr, rmarkdown, iheatmapr, mice, ropls,
ggplot2

person(given = "Eric",
family = "Scott",
role = c("aut", "cre"),
email = "scottericr@gmail.com",
comment =c(ORCID = "0000-0002-7430-7879"))
Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
with groups of variables with different degrees of variance, covariance, and effect size.
testthat, covr, knitr, rmarkdown, mice, ggplot2