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

Title: A Tidy Interface for Simulating Multivariate Data
Description: Provides pipe-friendly (%>%) functions to create simulated multivariate data sets
with groups of variables with different degrees of variance, covariance, and effect size.
Imports: dplyr, tibble, MASS, purrr, rlang, assertthat
Suggests: testthat, covr, knitr, rmarkdown, iheatmapr, mice, ropls,
Packaged: 2019-04-15 19:56:19 UTC; scottericr

Description: Provides pipe-friendly (%>%) wrapper functions for MASS::mvrnorm() to create simulated multivariate data sets
Suggests: testthat, covr, knitr, rmarkdown, mice, ggplot2
Packaged: 2020-05-25 23:00:30 UTC; scottericr
Author: Eric Scott [aut, cre] (<https://orcid.org/0000-0002-7430-7879>)

Author: Jan de Leeuw <deleeuw@stat.ucla.edu> with Arno Ouwehand (arno@stat.ucla.edu)
Description: Homogeneity Analysis (HOMALS) package with optional tcl/tk interface.
Tcltk, tkrplot needs to be installed for the tcltk interface to function.

Maintainer: Jan de Leeuw <deleeuw@ucla.edu>
Description: This package performs a homogeneity analysis, aka a multiple correspondence
analysis, but with many additional options. Variables can be grouped into
sets, in order to emulate regression analysis and canonical analysis. For
each variable rank constraints on the category and quantifications (or transformations) and level constraints (which allows one to treat a variable as nominal, ordinal, or numerical) can be imposed.
Packaged: Tue Oct 16 16:19:00 2007; patrick

Description: This package performs a homogeneity analysis and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
Packaged: Mon Feb 23 07:03:25 2009; admin

Description: This package performs a homogeneity analysis and various
extensions. Rank restrictions on the category quantifications
can be imposed (nonlinear PCA). The categories are transformed
by means of optimal scaling with options for nominal, ordinal,
and numerical scale levels (for rank-1 restrictions). Variables
can be grouped into sets, in order to emulate regression
analysis and canonical correlation analysis.