Correcting area under ROC (AUC) for measurement error based on probit-shift model.

There are many estimators of false discovery rate. In this package we compute the Nonlocal False
   Discovery Rate (NFDR) and the estimators of local false discovery rate: Corrected False discovery
   Rate (CFDR), Re-ranked False Discovery rate (RFDR) and the blended estimator. 
   Bickel, D.R., Rahal, A. (2019) <https://tinyurl.com/kkdc9rk8>.

Analyzes and modifies metabolomics raw data (generated using GC-APCI-MS, Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) to correct overloaded signals, i.e. ion intensities exceeding detector saturation leading to a cut-off peak. Data in xcmsRaw format are accepted as input and mzXML files can be processed alternatively. Overloaded signals are detected automatically and modified using an Gaussian or Isotopic-Ratio approach, QC plots are generated and corrected data are stored within the original xcmsRaw or mzXML respectively to allow further processing.

Calculate a set of corrected test statistics for cases when samples
    are not independent, such as when classification accuracy values are obtained
    over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> 
    and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

Lightweight package for computing different kinds
    of correlations, such as partial correlations, Bayesian correlations,
    multilevel correlations, polychoric correlations, biweight
    correlations, distance correlations and more. Part of the 'easystats'
    ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Speeds up exploratory data analysis (EDA)
    by providing a succinct workflow and interactive visualization tools for understanding
    which features have relationships to target (response). Uses binary correlation analysis
    to determine relationship. Default correlation method is the Pearson method. 
    Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.