  Although the statistical literature extensively covers continuous-valued time
series processes and their parametric, non-parametric and semiparametric
estimation, the literature on count data time series is considerably less
advanced. Among the count data time series models, the integer-valued
autoregressive (INAR) model is arguably the most popular one finding
applications in a wide variety of fields such as medical sciences,
environmentology and economics. While many contributions have been made during
the last decades, the majority of the literature focuses on parametric INAR
models and estimation techniques. Our emphasis is on the complex but efficient
and non-restrictive semiparametric estimation of INAR models. The appeal of
this approach lies in the absence of a commitment to a parametric family of
innovation distributions. In this paper, we describe the need and the features
of our R package spINAR which combines semiparametric simulation, estimation
and bootstrapping of INAR models also covering its parametric versions.
