Transferring over a code base from Matlab to R is often a repetitive
    and inefficient use of time. This package provides a translator for Matlab /
    Octave code into R code. It does some syntax changes, but most of the heavy
    lifting is in the function changes since the languages are so similar.
    Options for different data structures and the functions that can be changed
    are given. The Matlab code should be mostly in adherence to the standard
    style guide but some effort has been made to accommodate different number of
    spaces and other small syntax issues. This will not make the code more R
    friendly and may not even run afterwards. However, the rudimentary syntax,
    base function and data structure conversion is done quickly so that the
    maintainer can focus on changes to the design structure.

Simulate, manage, visualize, and analyze spatially and temporally 
    explicit datasets of mating potential. Implements methods to calculate 
    synchrony, proximity, and compatibility.Synchrony calculations are based on 
    methods described in Augspurger (1983) <doi:10.2307/2387650>, 
    Kempenaers (1993) <doi:10.2307/3676415>, Ison et al. (2014) 
    <doi:10.3732/ajb.1300065>, and variations on these, as described.

You can apply image processing effects that modifies the perceived material properties of objects
    in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by
    Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing".
    Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.

Implements the Multi-view Aggregated Two-Sample (MATES) test, a powerful nonparametric method for testing equality of two multivariate distributions. The method constructs multiple graph-based statistics from various perspectives (views) including different distance metrics, graph types (nearest neighbor graphs, minimum spanning trees, and robust nearest neighbor graphs), and weighting schemes. These statistics are then aggregated through a quadratic form to achieve improved statistical power. The package provides both asymptotic closed-form inference and permutation-based testing procedures. For methodological details, see Cai and others (2026+) <doi:10.48550/arXiv.2412.16684>.