A multivariate Gaussian mixture model framework to integrate multiple types of genomic data and allow modeling of inter-data-type correlations for association analysis. 'IMIX' can be implemented to test whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation, gene expression, etc. It can also study the integration of multiple pathways. 'IMIX' uses the summary statistics of association test outputs and conduct integration analysis for two or three types of genomics data. 'IMIX' features statistically-principled model selection, global FDR control and computational efficiency. Details are described in Ziqiao Wang and Peng Wei (2020) <doi:10.1093/bioinformatics/btaa1001>.

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.