Package: coconots 1.1.3
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 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>.
Authors:
coconots_1.1.3.tar.gz
coconots_1.1.3.zip(r-4.5)coconots_1.1.3.zip(r-4.4)coconots_1.1.3.zip(r-4.3)
coconots_1.1.3.tgz(r-4.4-x86_64)coconots_1.1.3.tgz(r-4.4-arm64)coconots_1.1.3.tgz(r-4.3-x86_64)coconots_1.1.3.tgz(r-4.3-arm64)
coconots_1.1.3.tar.gz(r-4.5-noble)coconots_1.1.3.tar.gz(r-4.4-noble)
coconots_1.1.3.tgz(r-4.4-emscripten)coconots_1.1.3.tgz(r-4.3-emscripten)
coconots.pdf |coconots.html✨
coconots/json (API)
# Install 'coconots' in R: |
install.packages('coconots', repos = c('https://manuhuth.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/manuhuth/coconots/issues
- cuts - Time Series of Monthly Counts of Claimants Collecting Wage Loss Benefit for Injuries in the Workplace
- downloads - Time Series of Daily Downloads of a TeX-Editor
- goldparticle - Time Series of Gold particles Counts in a well-efined Colloidal Solution
Last updated 3 days agofrom:85f2d90ef9. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 20 2024 |
R-4.5-win-x86_64 | NOTE | Nov 20 2024 |
R-4.5-linux-x86_64 | NOTE | Nov 20 2024 |
R-4.4-win-x86_64 | NOTE | Nov 20 2024 |
R-4.4-mac-x86_64 | NOTE | Nov 20 2024 |
R-4.4-mac-aarch64 | NOTE | Nov 20 2024 |
R-4.3-win-x86_64 | NOTE | Nov 20 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 20 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 20 2024 |
Exports:autoplotcocoBootcocoPitcocoRegcocoResidcocoScorecocoSimcocoSocinstallJuliaPackagessetJuliaSeed
Dependencies:clicolorspacecurlfansifarverforecastfracdiffgenericsggplot2gluegtableHMMpaisobandjsonliteJuliaConnectoRlabelinglatticelifecyclelmtestmagrittrMASSMatrixmatrixStatsmgcvmunsellnlmennetnumDerivpillarpkgconfigquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Concolution-closed Models for Time Series | coconots-package Concolution-closed Models for Count Time Series |
Bootstrap Based Model Assessment Procedure | cocoBoot |
Probability Integral Transform Based Model Assessment Procedure | cocoPit |
cocoReg | cocoReg |
Residual Based Model Assessment Procedure | cocoResid |
Scoring Rule Based Model Assessment Procedure | cocoScore |
Simulation of Count Time Series | cocoSim |
Compute Scores for Various Models | cocoSoc |
Time Series of Monthly Counts of Claimants Collecting Wage Loss Benefit for Injuries in the Workplace | cuts |
Time Series of Daily Downloads of a TeX-Editor | downloads |
Time Series of Gold particles Counts in a well-efined Colloidal Solution | goldparticle |
installJuliaPackages | installJuliaPackages |
K-Step Ahead Forecast Bootstrapping | predict.coco |
Set Seed for Julia's Random Number Generator | setJuliaSeed |