Biodiversity is in crisis. The overarching aim of conservation
    is to preserve biodiversity patterns and processes. To this end, protected
    areas are established to buffer species and preserve biodiversity processes.
    But resources are limited and so protected areas must be cost-effective.
    This package contains tools to generate plans for protected areas
    (prioritizations), using spatially explicit targets for biodiversity
    patterns and processes. To obtain solutions in a feasible amount  of time,
    this package uses the commercial 'Gurobi' software (obtained from
    <https://www.gurobi.com/>). For more information on using
    this package, see Hanson et al. (2018) <doi:10.1111/2041-210X.12862>.

Offers functions for fetching JSON data from the US EPA Air Quality
             System (AQS) API with options to comply with the API rate limits.
             See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             details of the AQS API.

Retrieve air monitoring data and associated metadata from the US
  Environmental Protection Agency's Air Quality System service using functions.
  See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details about
  the US EPA Data Mart API.

Generate a table of cumulative water influx into hydrocarbon reservoirs over time using un-steady and pseudo-steady state models. Van Everdingen, A. F. and Hurst, W. (1949) <doi:10.2118/949305-G>. Fetkovich, M. J. (1971) <doi:10.2118/2603-PA>. Yildiz, T. and Khosravi, A. (2007) <doi:10.2118/103283-PA>.

Perform risk-adjusted regression and sensitivity analysis as
    developed in "Mitigating Omitted- and Included-Variable Bias in Estimates of
    Disparate Impact" Jung et al. (2024) <arXiv:1809.05651>.

Implementation of an alternating direction method of multipliers
    algorithm for fitting a linear model with tree-based lasso regularization, 
    which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. 
    The package allows efficient model fitting on the entire 2-dimensional 
    regularization path for large datasets. The complete set of functions 
    also makes the entire process of tuning regularization parameters and 
    visualizing results hassle-free.