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>.
| Version: | 2.0.0 |
| Depends: | R (≥ 4.0.2), Rcpp |
| Imports: | forecast, numDeriv, HMMpa, ggplot2, matrixStats, JuliaConnectoR |
| LinkingTo: | Rcpp |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-03-22 |
| Author: | Manuel Huth [aut, cre], Robert C. Jung [aut], Andy Tremayne [aut] |
| Maintainer: | Manuel Huth <manuel.huth at yahoo.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Materials: | README |
| In views: | TimeSeries |
| CRAN checks: | coconots results |
| Reference manual: | coconots.pdf |
| Package source: | coconots_2.0.0.tar.gz |
| Windows binaries: | r-devel: coconots_1.1.3.zip, r-release: coconots_1.1.3.zip, r-oldrel: coconots_1.1.3.zip |
| macOS binaries: | r-devel (arm64): coconots_1.1.3.tgz, r-release (arm64): coconots_1.1.3.tgz, r-oldrel (arm64): coconots_1.1.3.tgz, r-devel (x86_64): 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 |
Please use the canonical form https://CRAN.R-project.org/package=coconots to link to this page.