Interpretability methods to analyze the behavior and
    predictions of any machine learning model.  Implemented methods are:
    Feature importance described by Fisher et al. (2018)
    <doi:10.48550/arxiv.1801.01489>, accumulated local effects plots described by Apley
    (2018) <doi:10.48550/arxiv.1612.08468>, partial dependence plots described by
    Friedman (2001) <www.jstor.org/stable/2699986>, individual conditional
    expectation ('ice') plots described by Goldstein et al.  (2013)
    <doi:10.1080/10618600.2014.907095>, local models (variant of 'lime')
    described by Ribeiro et. al (2016) <doi:10.48550/arXiv.1602.04938>, the Shapley
    Value described by Strumbelj et. al (2014)
    <doi:10.1007/s10115-013-0679-x>, feature interactions described by
    Friedman et. al <doi:10.1214/07-AOAS148> and tree surrogate models.

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Toolkit for Poly-pharmacology Research of Traditional Chinese Medicine. Based on the
    biological descriptors and drug-disease interaction networks, it can
    analyze the potential poly-pharmacological mechanisms of Traditional Chinese Medicine and be
    used for drug-repositioning in Traditional Chinese Medicine.

Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package. 
    See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>)
    for an overview of modeling alternatives.

Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "BIM"(Boosted version of IMMIGRATE algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.