Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <http://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

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.

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>.

The framework of causal decomposition of group disparities developed by
    Yu and Elwert (2023)
    <doi:10.48550/arXiv.2306.16591>.
    This package implements the decomposition estimators
    that are based on efficient influence functions. For the
    nuisance functions of the estimators, both parametric and
    nonparametric options are provided, as well as manual options in case
    the default models are not satisfying.