Performs Zoom-Focus Algorithm (ZFA) to optimize testing regions for rare variant association tests in exome sequencing data.

Improve the usage of model fitting functions within a piped
    work flow.

A two-part zero-inflated Beta regression model with random 
    effects (ZIBR) for testing the association between microbial abundance 
    and clinical covariates for longitudinal microbiome data. Eric Z. Chen 
    and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.

Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.

Parameter estimation for zero-inflated discrete Weibull (ZIDW) regression models, the univariate setting, distribution functions, functions to generate randomized quantile residuals a pseudo R2, and plotting of rootograms. For more details, see Kalktawi (2017)  <https://bura.brunel.ac.uk/handle/2438/14476>, Taconeli and Rodrigues de Lara (2022) <doi:10.1080/00949655.2021.2005597>, and Yeh and Young (2025) <doi:10.1080/03610918.2025.2464076>.

The 'Ziggurat' pseudo-random number generator (or PRNG),
 introduced by Marsaglia and Tsang (2000, <doi:10.18637/jss.v005.i08>) and
 further improved by Leong et al (2005, <doi:10.18637/jss.v012.i07>), offers
 a lightweight and very fast PRNG for the normal, exponential, and uniform
 distributions. It is provided here in a small zero-dependency package. It can
 be used from R as well as from 'C/C++' code in other packages as is demonstrated
 by four included sample packages using four distinct methods to use the PRNG
 presented here in client package. The implementation is influenced by our
 package 'RcppZiggurat' which offers a comparison among multiple alternative
 implementations but presented here in a lighter-weight implementation that is
 easier to use by other packages. The PRNGs provided are generally faster than
 the ones in base R: on our machine, the relative gains for normal, exponential
 and uniform are on the order of 7.4, 5.2 and 4.7 times faster than base R.
 However, these generators are of potentially lesser quality and shorter period
 so if in doubt use of the base R functions remains the general recommendation.