Tools for estimating, measuring and working with migration data.

A set of tools for analysing multimodal networks.
   It includes functions for measuring 
   centrality, centralization, cohesion, closure, constraint and diversity,
   as well as for network block-modelling, regression, and diffusion models.
   The package is released as a complement to 
   'Multimodal Political Networks' (2021, ISBN:9781108985000),
   and includes various datasets used in the book.
   Built on the 'manynet' package, all functions operate with matrices, edge lists, 
   and 'igraph', 'network', and 'tidygraph' objects,
   and on one-mode, two-mode (bipartite), and sometimes three-mode networks.

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>.