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>.

A comprehensive and computationally fast framework to analyze high dimensional data associated with an experimental design based on Multiple ANOVAs (MultANOVA). It includes testing the overall significance of terms in the model, post-hoc analyses of significant terms and variable selection. Details may be found in Mahieu, B., & Cariou, V. (2025). MultANOVA Followed by Post Hoc Analyses for Designed High‐Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA. Journal of Chemometrics, 39(7). <doi:10.1002/cem.70039>.