Harvest metadata using the Open Archives Initiative Protocol for Metadata
  Harvesting (OAI-PMH) version 2.0 (for more information, see
  <https://www.openarchives.org/OAI/openarchivesprotocol.html>).

A comprehensive set of helpers that streamline data transmission
  and processing, making it effortless to interact with the 'OpenAI' API.

Estimates propensity scores by the outcome-adaptive lasso of
    Shortreed and Ertefaie (2017) <doi:10.1111/biom.12679> and the generalized
    outcome-adaptive lasso (GOAL) of Balde, Yang and Lefebvre (2023)
    <doi:10.1111/biom.13683>, using 'glmnet' with an exact penalty-scale
    correction so that the published objectives and tuning grids are
    reproduced. Tuning is by the weighted absolute mean difference balance
    criterion. The resulting score is designed to be supplied directly to the
    matchit() function of 'MatchIt' as a distance measure, to the weightit()
    function of 'WeightIt' as a propensity score, or to the psave() function
    of 'psAve' as an appended candidate.

Offers a suite of functions for enhancing R plots.

Implements the efficient algorithm by Ortmann and Brandes (2017)
  <doi:10.1007/s41109-017-0027-2> to compute the orbit-aware frequency distribution of induced and non-induced quads, i.e. subgraphs of size four. Given an edge matrix, data frame, or a graph object (e.g., 'igraph'), the orbit-aware counts are computed respective each of the edges and nodes.

Generalise the starting point of the array index.

A comprehensive set of indexes and tests for social segregation analysis,
              as described in Tivadar (2019) - 'OasisR': An R Package to Bring Some Order
              to the World of Segregation Measurement <doi:10.18637/jss.v089.i07>.
              The package  is the most complete existing tool and it clarifies
              many ambiguities and errors regarding the definition of segregation
              indices. Additionally, 'OasisR' introduces several resampling methods
              that enable testing their statistical significance
              (randomization tests, bootstrapping, and jackknife methods).