Convolution-Closed Models for Count Time Series R package coconots version 2.0.3 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x> and, Tsay (1992) <doi:10.2307/2347612>. Software Depends: R (>= 4.0.2) Imports: forecast, numDeriv, HMMpa, ggplot2, matrixStats, JuliaConnectoR, Rcpp, stats LinkingTo: Rcpp Suggests: testthat (>= 3.0.0) Manuel Huth <manuel.huth@yahoo.com> Comprehensive R Archive Network (CRAN) Manuel Huth [aut, cre], Robert C. Jung [aut], Andy Tremayne [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=coconots/LICENSE) 2026-06-14 application/tgz https://CRAN.R-project.org/package=coconots doi:10.32614/CRAN.package.coconots