The BAGofT assesses the goodness-of-fit of binary classifiers. Details can be found in Zhang, Ding and Yang (2021) <arXiv:1911.03063v2>.

Tree- and rule-based models can be bagged
    (<doi:10.1007/BF00058655>) using this package and their predictions
    equations are stored in an efficient format to reduce the model
    objects size and speed.

The Philippines frequently experiences tropical cyclones (called
    'bagyo' in the Filipino language) because of its geographical position.
    These cyclones typically bring heavy rainfall, leading to widespread
    flooding, as well as strong winds that cause significant damage to human
    life, crops, and property. Data on cyclones are collected and curated by the
    Philippine Atmospheric, Geophysical, and Astronomical Services
    Administration or 'PAGASA' and made available through its website
    <https://bagong.pagasa.dost.gov.ph/tropical-cyclone/publications/annual-report>.
    This package contains Philippine tropical cyclones data in a
    machine-readable format. It is hoped that this data package provides an
    interesting and unique dataset for data exploration and visualisation.

A method to filter correlation and covariance matrices by averaging
     bootstrapped filtered hierarchical clustering and boosting. See Ch. Bongiorno and D. Challet,
     Covariance matrix filtering with bootstrapped hierarchies (2020) <arXiv:2003.05807> and
     Ch. Bongiorno and D. Challet, Reactive Global Minimum Variance Portfolios with k-BAHC covariance cleaning
     (2020) <arXiv:2005.08703>.

A Bayesian model for examining the association between
    environmental mixtures and all Taxa measured in a hierarchical
    microbiome dataset in a single integrated analysis. Compared with
    analyzing the associations of environmental mixtures with each Taxa
    individually, 'BaHZING' controls Type 1 error rates and provides more
    stable effect estimates when dealing with small sample sizes.

Computes approximated adjusted fractional Bayes factors for
    equality, inequality, and about equality constrained hypotheses.
    For a tutorial on this method, see Hoijtink, Mulder, van Lissa, & Gu,
    (2019) <doi:10.1037/met0000201>. For applications in structural equation
    modeling, see: Van Lissa, Gu, Mulder, Rosseel, Van Zundert, &
    Hoijtink, (2021) <doi:10.1080/10705511.2020.1745644>. For the statistical
    underpinnings, see Gu, Mulder, and Hoijtink (2018) <doi:10.1111/bmsp.12110>;
    Hoijtink, Gu, & Mulder, J. (2019) <doi:10.1111/bmsp.12145>; Hoijtink, Gu, 
    Mulder, & Rosseel, (2019) <doi:10.31234/osf.io/q6h5w>.