A compilation of fish stock assessment methods for the
    analysis of length-frequency data in the context of data-poor
    fisheries. Includes methods and examples included in the FAO
    Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish
    stock assessment" (<https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>),
    as well as other more recent methods.

Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.

The main function of the package aims to update 'lmer()'/'glmer()' models depending on their warnings, so trying to avoid convergence and singularity problems.

Provides methods for generating .dat files for use with the 'AMPL' 
    software using spatial data, particularly rasters. It includes support for 
    various spatial data formats and different problem types. By automating the 
    process of generating 'AMPL' datasets, this package can help streamline 
    optimization workflows and make it easier to solve complex optimization 
    problems. The methods implemented in this package are described in detail
    in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).

Bayesian trophic position models using 'stan' by leveraging 'brms' for 
             stable isotope data. Trophic position models are derived by using 
             equations from 
             Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>,
             Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, 
             and 
             Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.

Gives the required 2^n treatment combinations in a 2^n symmetric factorial experiment in their respective standard order.

Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).