A set of tools for testing networks.
   It includes functions for univariate and multivariate conditional uniform graph 
   and quadratic assignment procedure testing, and network regression.
   The package is 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 and two-mode (bipartite) networks.

Tools to help convert credit risk data at two timepoints
    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) <doi:10.48550/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.

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>.

Data-driven approach for Exploratory Factor Analysis (EFA)
    that uses Model Implied Instrumental Variables (MIIVs). The method
    starts with a one factor model and arrives at a suggested model with
    enhanced interpretability that allows cross-loadings and correlated
    errors.