Provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE)
    analysis (Law (2015) <doi:10.12688/f1000research.9005.3>).  The tools include both analysis 
    work-flow and utility functions: mapping/unit conversion, count normalization, accounting for 
    unknown covariates, and more.  This is a complement/cohort to the 'DGEobj' package that 
    provides a flexible container to manage and annotate Differential Gene Expression analysis results.

Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.

Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.

Model fitting and evaluation tools for double generalized linear
    models (DGLMs). This class of models uses one generalized linear model (GLM)
    to fit the specified response and a second GLM to fit the deviance of the first
    model.

Model estimation, dispersion testing and diagnosis of hyper-Poisson
    Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) 
    <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017)
    regression models.

A revision to the stats::ks.test() function and the associated
        ks.test.Rd help page. With one minor exception, it does not change the
        existing behavior of ks.test(), and it adds features necessary
        for doing one-sample tests with hypothesized discrete
        distributions. The package also contains cvm.test(), for doing
        one-sample Cramer-von Mises goodness-of-fit tests.