Fast Bayesian estimation and forecasting of age-specific
    rates, probabilities, and means, based on 'Template Model Builder'.

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These 
  bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being 
    computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and 
  Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.

Running and comparing meta-analyses of data with hierarchical 
    Bayesian models in Stan, including convenience functions for formatting
    data, plotting and pooling measures specific to meta-analysis. This implements many models
    from Meager (2019) <doi:10.1257/app.20170299>.

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.