An S4 implementation of the unbiased extension of
    the model- assisted synthetic-regression estimator proposed by
    Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013)
    <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014)
    <DOI:10.1139/cjfr-2013-0449>.  It yields smaller variances than the
    standard bias correction, the generalised regression estimator.

Implements a procedure to automatically generate 2D masks
    for clusters on dimensional reduction plots from methods like 
    t-SNE (t-distributed stochastic neighbor embedding) or 
    UMAP (uniform manifold approximation and projection),
    with a focus on single-cell RNA-sequencing data.

A set of model-assisted survey estimators and corresponding
    variance estimators for single stage, unequal probability, without replacement
    sampling designs.  All of the estimators can be written as a generalized 
    regression estimator with the Horvitz-Thompson, ratio, post-stratified, and 
    regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
    Two of the estimators employ a statistical learning model as the assisting model:
    the elastic net regression estimator, which is an extension of the lasso regression
    estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the 
    regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. 
    The variance estimators which approximate the joint inclusion probabilities can
    be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
    bootstrap variance estimator is presented in Mashreghi et al. (2016) 
    <doi:10.1214/16-SS113>.

Implements the multivariate adaptive shrinkage (mash)
    method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
    estimating and testing large numbers of effects in many conditions
    (or many outcomes). Mash takes an empirical Bayes approach to
    testing and effect estimation; it estimates patterns of similarity
    among conditions, then exploits these patterns to improve accuracy
    of the effect estimates. The core linear algebra is implemented in
    C++ for fast model fitting and posterior computation.