A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)<https://github.com/Lujun995/DiSC_Replication_Code>.

Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.

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.