Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.

Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis, by Montgomery, Peck
             and Vining.  All data sets from the 3rd edition are included
             and many from the 6th edition are also included.
             The package also contains some additional data sets and functions.

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.