This is a companion to the book Cook, D. and Laa, U. (2023) <https://dicook.github.io/mulgar_book/>
  "Interactively exploring high-dimensional data and models in R".  
  by Cook and Laa. It contains useful functions for processing data in preparation for 
  visualising with a tour. There are also several sample data sets.

Allows the user to create graphs with multiple layers. The user can also modify the layers, the nodes, and the edges. The graph can also be visualized.
    Zaynab Hammoud and Frank Kramer (2018) <doi:10.3390/genes9110519>.
    More about multilayered graphs and their usage can be found in our review paper:
    Zaynab Hammoud and Frank Kramer (2020) <doi:10.1186/s41044-020-00046-0>.

A set of functions for some multivariate analyses utilizing a
             structural equation modeling (SEM) approach through the 'OpenMx' package.
             These analyses include canonical correlation analysis (CANCORR),
             redundancy analysis (RDA), and multivariate principal component regression (MPCR).
             It implements procedures discussed in Gu and Cheung (2023) <doi:10.1111/bmsp.12301>,
             Gu, Yung, and Cheung (2019) <doi:10.1080/00273171.2018.1512847>, and
             Gu et al. (2023) <doi:10.1080/00273171.2022.2141675>.

Computes efficient data distributions from highly inconsistent datasets with many missing values using multi-set intersections. Based upon hash functions, 'mulset' can quickly identify intersections from very large matrices of input vectors across columns and rows and thus provides scalable solution for dealing with missing values. Tomic et al. (2019) <doi:10.1101/545186>.

Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.