Provides functions for the creation, evaluation and test of decision models based in
    Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
    for a set of indexes, compute utilities and weights for the whole decision tree defining the
    decision model and simulate weights employing Dirichlet distributions under addition constraints 
    in weights.

'BEAST2' (<https://www.beast2.org>) is a widely used
    Bayesian phylogenetic tool, that uses DNA/RNA/protein data
    and many model priors to create a posterior of jointly estimated 
    phylogenies and parameters.
    'BEAST2' is commonly accompanied by 'BEAUti 2' (<https://www.beast2.org>),
    which, among others, allows one to install 'BEAST2' package. 
    This package allows to work with 'BEAST2' packages from 'R'.

Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included. Xia (2002) <doi:10.1111/1467-9868.03411>; Xia (2007) <doi:10.1214/009053607000000352>; Wang (2008) <doi:10.1198/016214508000000418>.

Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.

Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
    most important set of uncorrelated variables and to fine-tune Maxent's
    regularization multiplier. In combination, this allows to constrain
    complexity and increase performance of Maxent niche models (assessed
    by information criteria, such as AICc (Akaike, 1974
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.