Contains the normalizing and variance stabilizing
	Data-Driven Haar-Fisz algorithm. Also contains related algorithms
	for simulating from certain microarray gene intensity models and
	evaluation of certain transformations. Contains cDNA and shipping
	credit flow data.

Implements Meng's data defect index (ddi), which represents
    the degree of sample bias relative to an iid sample. The data defect 
    correlation (ddc) represents the correlation between the outcome of interest
    and the selection into the sample; when the sample selection is independent
    across the population, the ddc is zero. Details are in Meng (2018) 
    <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): 
    Law of Large Populations, Big Data Paradox, and the 2016 US Presidential 
    Election." Survey estimates from the Cooperative Congressional Election Study 
    (CCES) is included to replicate the article's results.

The Data Driven I-V Feature Extraction is used to extract 
    Current-Voltage (I-V) features from I-V curves. I-V curves indicate the 
    relationship between current and voltage for a solar cell or Photovoltaic (PV) 
    modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), 
    series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), 
    fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) 
    contain important information of the performance for PV modules. The traditional 
    method uses the single diode model to model I-V curves and extract I-V features. 
    This package does not use the diode model, but uses data-driven a method which 
    select different linear parts of the I-V curves to extract I-V features. 
    This method also uses a sampling method to calculate uncertainties when extracting 
    I-V features. Also, because of the partially shaded array, "steps" occurs in 
    I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves.
    This material is based upon work supported by the U.S. Department of Energy’s 
    Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy 
    Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be 
    found in the following paper.
    [1] Ma, X. et al, 2019. 
    <doi:10.1109/JPHOTOV.2019.2928477>.