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.

Provides a lightweight delayed array abstraction for lazy,
    fused evaluation of multi-dimensional numeric data. Compared with
    Bioconductor's 'DelayedArray', it offers a small S3 API with tidy-friendly
    verbs, chunked materialisation, and optional backends for HDF5, memory
    mapping, and shared-memory parallelism. Expression trees are optimised
    before streaming evaluation, and results can be written directly to disk
    without fully materialising arrays in memory.

Functions for estimating parameters and hyperparameters of the
  linearized hyperbolic model, and testing equality of hyperparameters.

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>.