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.

Aggregates matrix population models (MPMs) in both the lambda (stable 
    growth rate) and R0 (net reproductive rate) frameworks, including standard and 
    elasticity-consistent aggregators. Standard aggregation in the lambda framework 
    maintains consistent lambda and stable stage distribution, while standard aggregation 
    in the R0 framework maintains consistent R0 and cohort stable stage distribution. 
    Elasticity-consistent aggregators maintain these same consistencies with respect
    to the chosen framework and additionally preserve consistent reproductive values in 
    the lambda framework and cohort reproductive values in the R0 framework. Aggregation 
    can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie
    MPM (leslie_aggregate).

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.