Implements a generalized linear model approach for detecting
    differentially expressed genes across treatment groups in count data. The
    package supports both quasi-Poisson and negative binomial models to handle
    over-dispersion, ensuring robust identification of differential expression.
    It allows for the inclusion of treatment effects and gene-wise covariates, 
    as well as normalization factors for accurate scaling across samples.
    Additionally, it incorporates statistical significance testing with
    options for p-value adjustment and log2 fold range thresholds,
    making it suitable for RNA-seq analysis as described in by 
    Xu et al., (2024) <doi:10.1371/journal.pone.0300565>.

Performs reference based multiple imputation of recurrent event data
    based on a negative binomial regression model, as described
    by Keene et al (2014) <doi:10.1002/pst.1624>.

Provides probability mass, distribution, quantile, random-variate
    generation, and method-of-moments parameter-estimation functions for the
    Delaporte distribution with parameterization based on Vose (2008)
    <isbn:9780470512845>. The Delaporte is a discrete probability distribution
    which can be considered the convolution of a negative binomial distribution
    with a Poisson distribution. Alternatively, it can be considered a counting
    distribution with both Poisson and negative binomial components. It has been
    studied in actuarial science as a frequency distribution which has more
    variability than the Poisson, but less than the negative binomial.

Mechanisms to parallelize dependent tasks in a manner that
    optimizes the compute resources available. It provides access to "delayed"
    computations, which may be parallelized using futures. It is, to an extent,
    a facsimile of the 'Dask' library (<https://www.dask.org/>), for the 'Python'
    language.

Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is either homogeneous across the individual subject, or varies heterogeneously from individual to individual within a certain domain and following a specific pattern. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.