Connect to the California Data Exchange Center (CDEC) 
    Web Service <http://cdec.water.ca.gov/>. 'CDEC' provides a centralized 
    database to store, process, and exchange real-time hydrologic information 
    gathered by various cooperators throughout California. The 'CDEC' Web Service 
    <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download 
    service for accessing historical records.

Implements the Centroid Decision Forest (CDF) as a single user-facing
  function CDF(). The method selects discriminative features via a multi-class
  class separability score (CSS), splits by nearest class centroid, and aggregates
  tree votes to produce predictions and class probabilities. Returns CSS-based
  feature importance as well. Amjad Ali, Saeed Aldahmani, Zardad Khan (2025) <doi:10.48550/arXiv.2503.19306>.

Estimation of one- and two-sided confidence intervals 
    via the numerical inversion of the cumulative distribution function
    of a statistic's sampling distribution. For more details, see
    section 9.2.3 of Casella and Berger (2002) <ISBN:0534243126>.

Employs a two-parameter family of
    distributions for modelling random variables on the (0, 1) interval by
    applying the cumulative distribution function (cdf) of one parent
    distribution to the quantile function of another.

Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.

Estimates the causal decompositions of group disparities developed by Yu and Elwert (2025) <doi:10.1214/24-AOAS1990>. For the nuisance functions of the estimators, we provide both parametric and nonparametric options, as well as manual options in case the default models are not satisfying.