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.

Provides tools for estimating Zero-Inflated INAR(1) 
    (ZI-INAR(1)) and Hurdle INAR(1) (H-INAR(1)) models using 'Stan'. 
    It allows users to simulate time series data for these models, 
    estimate parameters, and evaluate model fit using various criteria. 
    Functions include model estimation, simulation, and likelihood-based metrics.

Analyze count time series with excess zeros. 
    Two types of statistical models are supported: Markov regression by Yang et al.
    (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. 
    (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and 
    parameter-driven models respectively in the time series literature. The functions used for 
    Markov regression or observation-driven models can also be used to fit ordinary regression models 
    with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) 
    assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, 
    quantile, and generate random numbers from ZIP and ZINB distributions.