Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
| Version: | 1.4.3 |
| Imports: | parallel, ltsa |
| Suggests: | Matrix, xtable, gamlss.data, surveillance |
| Published: | 2020-09-08 |
| Author: | Tobias Liboschik [aut, cre], Roland Fried [aut], Konstantinos Fokianos [aut], Philipp Probst [aut], Jonathan Rathjens [ctb] |
| Maintainer: | Tobias Liboschik <liboschik at statistik.tu-dortmund.de> |
| License: | GPL-2 | GPL-3 |
| URL: | http://tscount.r-forge.r-project.org |
| NeedsCompilation: | no |
| Citation: | tscount citation info |
| Materials: | NEWS |
| In views: | TimeSeries |
| CRAN checks: | tscount results |
| Reference manual: | tscount.pdf |
| Vignettes: |
tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models |
| Package source: | tscount_1.4.3.tar.gz |
| Windows binaries: | r-devel: tscount_1.4.3.zip, r-release: tscount_1.4.3.zip, r-oldrel: tscount_1.4.3.zip |
| macOS binaries: | r-release (arm64): tscount_1.4.3.tgz, r-oldrel (arm64): tscount_1.4.3.tgz, r-release (x86_64): tscount_1.4.3.tgz, r-oldrel (x86_64): tscount_1.4.3.tgz |
| Old sources: | tscount archive |
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