coconots: Convolution-Closed Models for Count Time Series

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 modeled via Poisson and Generalized Poisson innovations. Regression effects can be modelled via 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>, Gneiting and Raftery (2007) <doi:10.1198/016214506000001437> and, Tsay (1992) <doi:10.2307/2347612>.

Version: 1.1.3
Depends: R (≥ 3.5.0)
Imports: Rcpp, forecast, numDeriv, HMMpa, stats, ggplot2, utils, matrixStats, JuliaConnectoR
LinkingTo: Rcpp, StanHeaders (≥ 2.21.0), RcppParallel (≥ 5.0.1)
Suggests: covr, testthat (≥ 3.0.0)
Published: 2023-10-01
DOI: 10.32614/CRAN.package.coconots
Author: Manuel Huth [aut, cre], Robert C. Jung [aut], Andy Tremayne [aut]
Maintainer: Manuel Huth <manuel.huth at>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
In views: TimeSeries
CRAN checks: coconots results


Reference manual: coconots.pdf


Package source: coconots_1.1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): coconots_1.1.3.tgz, r-oldrel (arm64): coconots_1.1.3.tgz, r-release (x86_64): coconots_1.1.3.tgz, r-oldrel (x86_64): coconots_1.1.3.tgz
Old sources: coconots archive


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