Implements non-parametric analyses for clustered
    binary and multinomial data. The elements of the cluster are assumed
    exchangeable, and identical joint distribution (also known as marginal
    compatibility, or reproducibility) is assumed for clusters of different
    sizes. A trend test based on stochastic ordering is implemented. 
    Szabo A, George EO. (2010) <doi:10.1093/biomet/asp077>;
    George EO, Cheon K, Yuan Y, Szabo A (2016) <doi:10.1093/biomet/asw009>.

Using a computationally efficient method, the package can
    be used to find the corrected coverage estimate of a credible set 
    of putative causal variants from Bayesian genetic fine-mapping. 
    The package can also be used to obtain a corrected credible set
    if required; that is, the smallest set of variants required such 
    that the corrected coverage estimate of the resultant credible set is  
    within some user defined accuracy of the desired coverage.
    Maller et al. (2012) <doi:10.1038/ng.2435>,
    Wakefield (2009) <doi:10.1002/gepi.20359>,
    Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.

Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.

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.