Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.

Implements an approach aimed at assessing the accuracy and
    effectiveness of raw scores obtained in scales that contain locally dependent
    items. The program uses as input the calibration (structural) item estimates
    obtained from fitting extended unidimensional factor-analytic solutions in
    which the existing local dependencies are included. Measures of reliability
    (Omega) and information are proposed at three levels: (a) total score, (b)
    bivariate-doublet, and (c) item-by-item deletion, and  are compared to those
    that would be obtained if all the items had been locally independent. All the
    implemented procedures can be obtained from: (a) linear factor-analytic
    solutions in which the item scores are treated as approximately continuous,
    and (b) non-linear solutions in which the item scores are treated as
    ordered-categorical. A detailed guide can be obtained at the following url.

A single, phenome-wide permutation of large-scale biobank data.
    When a large number of phenotypes are analyzed in parallel, a single 
    permutation across all phenotypes followed by genetic association analyses 
    of the permuted data enables estimation of false discovery rates (FDRs) 
    across the phenome. These FDR estimates provide a significance criterion 
    for interpreting genetic associations in a biobank context. For the basic
    permutation of unrelated samples, this package takes a sample-by-variable 
    file with ID, genotypic covariates, phenotypic covariates, and phenotypes 
    as input. For data with related samples, it also takes a file with 
    sample pair-wise identity-by-descent information. 
    The function outputs a permuted sample-by-variable file ready 
    for genome-wide association analysis. See Annis et al. (2021) <doi:10.21203/rs.3.rs-873449/v1> 
    for details.