Software to support the introductory *MOSAIC Calculus* 
    textbook <https://www.mosaic-web.org/MOSAIC-Calculus/>),
    one of many data- and modeling-oriented educational resources developed by 
    Project MOSAIC (<https://www.mosaic-web.org/>). Provides symbolic and
    numerical differentiation and integration, as well as support for 
    applied linear algebra (for data science), and differential equations/dynamics.
    Includes grammar-of-graphics-based functions for drawing vector fields, trajectories, etc.
    The software is suitable for general use, but intended mainly for teaching calculus.

Common utilities used in other MOSAIC-family packages are 
    collected here.

Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.

An umbrella framework ("MoSaIC" = Multi-Omics Source-Agnostic
    Integration Clustering in R) that unifies a large collection
    of multi-source / multi-omics clustering methodologies behind a single,
    consistent list-of-matrices interface. It spans five integration
    paradigms - direct, similarity-based, graph-based, voting-based consensus,
    and hierarchy-based - and bundles a complete downstream workflow for
    method comparison and evaluation. The package features the multi-source
    the ability to compare many algorithms on the same footing, a data-nugget
    based feature-weighting scheme as a robust, big-data-friendly alternative to
    variance weighting, and a downstream suite for cluster characterisation,
    visualisation and biological interpretation.

Provides a framework for multipurpose optimal resource allocation in survey sampling,
    extending the classical optimal allocation principles introduced by Tschuprow (1923) and
    Neyman (1934) to multidomain and multivariate allocation problems. The primary method
    mosalloc() allows for the consideration of precision and cost constraints at the subpopulation
    level while minimizing either a vector of sampling errors or survey costs across a broad range
    of optimal sample allocation problems. The approach supports both single- and multistage
    designs. For single-stage stratified random sampling, the mosallocSTRS() function offers a user-
    friendly interface. Sensitivity analysis is supported through the problem's dual variables, which
    are naturally obtained via the internal use of the Embedded Conic Solver from the 'ECOSolveR'
    package. See Willems (2025, <doi:10.25353/ubtr-9200-484c-5c89>) for a detailed description
    of the theory behind 'MOSAlloc'.