Provides functionality for estimating cross-sectional network structures representing partial correlations in R, while accounting for missing values in the data. Networks are estimated via neighborhood selection, i.e., node-wise multiple regression, with model selection guided by information criteria. Missing data can be handled primarily via multiple imputation or a maximum likelihood-based approach; deletion techniques are available but secondary <doi:10.31234/osf.io/qpj35>.

Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server.

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".