Title: Statistical Functions for the Delaporte Distribution

Description: Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. 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.

Authors@R: c(person(given="Avraham", family="Adler", role=c("aut", "cph", "cre"), email="Avraham.Adler@gmail.com", comment = c(ORCID = "0000-0002-3039-0703")))
Author: Avraham Adler [aut, cph, cre] (<https://orcid.org/0000-0002-3039-0703>)

Authors@R: c(person(given="Avraham", family="Adler", role=c("aut", "cph", "cre"),
email="Avraham.Adler@gmail.com", comment = c(ORCID = "0000-0002-3039-0703")))
Description: 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.

Title: Sample Size and Power Calculations using the APPLE and SEPPLE
Author: Zhenzhen Xu <Zhenzhen.Xu@fda.hhs.gov> , Boguang Zhen<Boguang.Zhen@fda.hhs.gov>, Yongsoek Park <yongpark@pitt.edu> and Bin Zhu <bin.zhu@nih.gov>
Description: Provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is homogeneous across the individual subject. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.
Packaged: 2017-09-01 14:15:16 UTC; wheelerwi