Implementations of an estimator for the multivariate regression association measure (MRAM) proposed in Shih and Chen (2026) <doi:10.1016/j.csda.2025.108288> and its associated variable selection algorithm. The MRAM quantifies the predictability of a random vector Y from a random vector X given a random vector Z. It takes the maximum value 1 if and only if Y is almost surely a measurable function of X and Z, and the minimum value of 0 if Y is conditionally independent of X given Z. The MRAM generalizes the Kendall's tau copula correlation ratio proposed in Shih and Emura (2021) <doi:10.1016/j.jmva.2020.104708> by employing the spatial sign function. The estimator is based on the nearest neighbor method, and the associated variable selection algorithm is adapted from the feature ordering by conditional independence (FOCI) algorithm of Azadkia and Chatterjee (2021) <doi:10.1214/21-AOS2073>. For further details, see the paper Shih and Chen (2026) <doi:10.1016/j.csda.2025.108288>.

Flexible, mechanistic, and spatially explicit simulator of
    metacommunities. It extends our previous package - 'rangr' (see
    <https://github.com/ropensci/rangr>), which implemented a mechanistic
    virtual species simulator integrating population dynamics and
    dispersal. The 'mrangr' package adds the ability to simulate multiple
    species interacting through an asymmetric matrix of pairwise
    relationships, allowing users to model all types of biotic
    interactions — competitive, facilitative, or neutral — within
    spatially explicit virtual environments. This work was supported by 
    the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and 
    the Poznań Supercomputing and Networking Centre (grant no. pl0090-01).

You can use the set of wrappers for analytical schemata 
    to reduce the effort in writing machine-readable data. The set of all-in-one
    wrappers will cover widely used functions from data analysis packages.

Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.