Comprehensive toolkit for addressing selection 
    bias in binary disease models across diverse non-probability samples, each 
    with unique selection mechanisms. It utilizes Inverse Probability Weighting 
    (IPW) and Augmented Inverse Probability Weighting (AIPW) methods to reduce 
    selection bias effectively in multiple non-probability cohorts by integrating 
    data from either individual-level or summary-level external sources. The 
    package also provides a variety of variance estimation techniques. Please 
    refer to Kundu et al. <doi:10.48550/arXiv.2412.00228>.

Implements methods for functional data analysis based on the epigraph 
  and hypograph indices. These methods transform 
  functional datasets, whether in one or multiple dimensions, into multivariate 
  datasets. The transformation involves applying the epigraph, hypograph, and 
  their modified versions to both the original curves and their first and second 
  derivatives. The calculation of these indices is tailored to the dimensionality 
  of the functional dataset, with special considerations for dependencies between 
  dimensions in multidimensional cases. This approach extends traditional multivariate
  data analysis techniques to the functional data setting. A key application of 
  this package is the EHyClus method, which enhances clustering analysis for 
  functional data across one or multiple dimensions using the epigraph and 
  hypograph indices. See Pulido et al. (2023) <doi:10.1007/s11222-023-10213-7>
  and Pulido et al. (2024) <doi:10.48550/arXiv.2307.16720>.

Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.

Provides more than 550 data sets of actual election results. 
    Each of the data sets includes aggregate party and candidate outcomes at the voting
    unit (polling stations) level and two-way cross-tabulated results at the district level.
    These data sets can be used to assess ecological inference algorithms devised 
    for estimating RxC (global) ecological contingency tables using exclusively aggregate results 
    from voting units.
    Reference:
    Pavía (2022) <doi:10.1177/08944393211040808>.