'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.
    'mcbette' allows to do a Bayesian model comparison over some
    site and clock models, 
    using 'babette' (<https://github.com/ropensci/babette/>).

Computes the prime implicants or a minimal disjunctive normal form for a
  logic expression presented by a truth table or a logic tree. Has been particularly 
  developed for logic expressions resulting from a logic regression analysis, i.e.
  logic expressions typically consisting of up to 16 literals, where the prime implicants 
  are typically composed of a maximum of 4 or 5 literals.

Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and
    'Multi-Accuracy Boosting' (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction.
    'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups.
    Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are.
    This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations.

It contains six common multi-category classification accuracy evaluation measures.
 All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically,
 Hypervolume Under Manifold (HUM), described in
 Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
 Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
 Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
 Polytomous Discrimination Index (PDI), described in
 Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>.
 Li et al. (2018) <doi:10.1177/0962280217692830>.
 We described all these above measures and our mcca package in
 Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.