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 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.
- Version2.0.0
- R versionR (≥ 4.0.2)
- LicenseMIT
- LicenseLICENSE
- Needs compilation?Yes
- Jung and Tremayne (2011)
- Czado et al. (2009)
- Gneiting and Raftery (2007)
- Tsay (1992)
- Last release03/22/2025
Documentation
Team
Manuel Huth
MaintainerShow author detailsRobert C. Jung
Show author detailsRolesAuthorAndy Tremayne
Show author detailsRolesAuthor
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