Tools to help convert credit risk data at two time points 
    into traditional credit state migration (aka, "transition") matrices.
    At a higher level, 'migrate' is intended to help an analyst understand 
    how risk moved in their credit portfolio over a time interval. 
    References to this methodology include: 
    1. Schuermann, T. (2008) <doi:10.1002/9780470061596.risk0409>.
    2. Perederiy, V. (2017) <arXiv:1708.00062>.

Calculate various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.

Detection of migration events and segments of continuous residence 
    based on irregular time series of location data
    as published in Chi et al. (2020) <doi:10.1371/journal.pone.0239408>.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

We report an information-theoretic method which learns a large
    class of causal or non-causal graphical models from purely observational
    data, while including the effects of unobserved latent variables, commonly
    found in many datasets. Starting from a complete graph, the method
    iteratively removes dispensable edges, by uncovering significant information
    contributions from indirect paths, and assesses edge-specific confidences
    from randomization of available data. The remaining edges are then oriented
    based on the signature of causality in observational data. This approach can
    be applied on a wide range of datasets and provide new biological insights
    on regulatory networks from single cell expression data, genomic alterations
    during tumor development and co-evolving residues in protein structures.
    For more information you can refer to:
    Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>,
    Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.

Contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response.
             Reference for mean score method, inverse probability weighted method is
             Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.