Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. It facilitates streamlined integration between 'Mplus' and 'R', allowing users to run and manage multiple 'Mplus' models simultaneously and efficiently in 'R'.

Uses recursive partitioning to create homogeneous subgroups based on structural equation models 
             fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.

Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.

Functions to compute and plot multivariate (partial) Mantel correlograms.

Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.

Allows users to simulate matrix population models with
    particular characteristics based on aspects of life history such as
    mortality trajectories and fertility trajectories. Also allows the
    exploration of sampling error due to small sample size.

Calculates the Most Probable Number (MPN) to quantify the
    concentration (density) of microbes in serial dilutions of a laboratory
    sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>).
    Also calculates the Aerobic Plate Count (APC) for similar microbial
    enumeration experiments.

Symbolic computing with multivariate polynomials in R.

A flexible framework for power analysis using Monte
    Carlo simulation for settings in which considerations of the correlations
    between predictors are important. Users can set up a data generative model
    that preserves dependence structures among predictors given existing data
    (continuous, binary, or ordinal). Users can also generate power curves to
    assess the trade-offs between sample size, effect size, and power of a design.
    This package includes several statistical models common in environmental
    mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.