Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.

Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut',
    'DIA-NN' and 'MaxQuant'.

Complements the book 
    "Using R for Modelling and Quantitative Methods in Fisheries"
    ISBN 9780367469894, published in 
    2021 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.

Parametric modeling of M-quantile regression coefficient functions.

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).