Provides efficient implementations of weighted dependence measures
  and related asymptotic tests for independence. Implemented measures are
  the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and
  Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and
  Hollander et al. (2015, ISBN:9780470387375).

There are a number of binary files associated with the
    'Webdriver'/'Selenium' project. This package provides functions to download
    these binaries and to manage processes involving them.

Assortativity coefficients, centrality measures, 
    and clustering coefficients for weighted and directed networks.
    Rewiring unweighted networks with given assortativity coefficients.
    Generating general preferential attachment networks.

Functions for finding and pulling data from the
  'Wisconsin Department of Natural Resources ArcGIS REST APIs'
  <https://dnrmaps.wi.gov/arcgis/rest/services> and 
  <https://dnrmaps.wi.gov/arcgis2/rest/services>.

Fetch and clean data from the World Database on Protected
    Areas (WDPA) and the World Database on Other Effective Area-Based
    Conservation Measures (WDOECM). Data is obtained from Protected Planet
    <https://www.protectedplanet.net/en>. To augment data cleaning procedures,
    users can install the 'prepr' R package (available at
    <https://github.com/prioritizr/prepr>). For more information on this
    package, see Hanson (2022) <doi:10.21105/joss.04594>.

Implements weighted double score matching (WDSM) for estimating
    population-level causal effects from complex survey data. Combines
    propensity scores and prognostic scores with survey design weights for
    matching, survey-weighted imputation within match sets, and Hajek
    normalization to target the population average treatment effect (PATE) and
    the population average treatment effect on the treated (PATT). Supports
    both retrospective (treatment-dependent) and prospective
    (treatment-independent) sampling designs. Achieves double robustness:
    consistent estimation when either the propensity score or prognostic score
    model is correctly specified. Provides polynomial sieve bias correction
    and linearization-based multinomial bootstrap variance estimation that
    preserves the survey-weighted matching structure without re-matching.
    Methods are described in Zeng, Tong, Tong, Lu, Mukherjee, and Li
    (2026, under review) "Where to weight? Estimating population causal
    effects with weighted double score matching in complex surveys".