This is the core package offering a portal to the many packages universe.
    It includes functions to help researchers access, work across, and maintain
    ensembles of datasets on global governance called datacubes.

A comprehensive framework for calculating unbiased distances in datasets 
    containing mixed-type variables (numerical and categorical). The package implements 
    a general formulation that ensures multivariate additivity and commensurability, 
    meaning that variables contribute equally to the overall distance regardless of 
    their type, scale, or distribution. Supports multiple distance measures including 
    Gower's distance, Euclidean distance, Manhattan distance, and various categorical 
    variable distances such as simple matching, Eskin, occurrence frequency, and 
    association-based distances. Provides tools for variable scaling (standard 
    deviation, range, robust range, and principal component scaling), and handles 
    both independent and association-based category dissimilarities. Implements 
    methods to correct for biases that typically arise from different variable types, 
    distributions, and number of categories. Particularly useful for cluster analysis, 
    data visualization, and other distance-based methods when working with mixed data. 
    Methods based on van de Velden et al. (2024) <doi:10.48550/arXiv.2411.00429> 
    "Unbiased mixed variables distance".

Comprehensive toolkit for Environmental Phillips Curve analysis 
    featuring multidimensional instrumental variable creation, transfer entropy 
    causal discovery, network analysis, and state-of-the-art econometric methods.
    Implements geographic, technological, migration, geopolitical, financial, 
    and natural risk instruments with robust diagnostics and visualization.
    Provides 24 different instrumental variable approaches with empirical validation.
    Methods based on Phillips (1958) <doi:10.1111/j.1468-0335.1958.tb00003.x>,
    transfer entropy by Schreiber (2000) <doi:10.1103/PhysRevLett.85.461>, and 
    weak instrument tests by Stock and Yogo (2005) <doi:10.1017/CBO9780511614491.006>.