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:Manuel Huth [aut, cre], Robert C. Jung [aut], Andy Tremayne [aut]

coconots_1.1.3.tar.gz
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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)
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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

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

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

On CRAN:

10 exports 2 stars 1.40 score 49 dependencies 4 scripts 257 downloads

Last updated 3 months agofrom:9ad457532a. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 27 2024
R-4.5-win-x86_64	NOTE	Aug 27 2024
R-4.5-linux-x86_64	NOTE	Aug 27 2024
R-4.4-win-x86_64	NOTE	Aug 27 2024
R-4.4-mac-x86_64	NOTE	Aug 27 2024
R-4.4-mac-aarch64	NOTE	Aug 27 2024
R-4.3-win-x86_64	NOTE	Aug 27 2024
R-4.3-mac-x86_64	NOTE	Aug 27 2024
R-4.3-mac-aarch64	NOTE	Aug 27 2024

Exports:autoplot cocoBoot cocoPit cocoReg cocoResid cocoScore cocoSim cocoSoc installJuliaPackages setJuliaSeed

Dependencies:cli colorspace curl fansi farver forecast fracdiff generics ggplot2 glue gtable HMMpa isoband jsonlite JuliaConnectoR labeling lattice lifecycle lmtest magrittr MASS Matrix matrixStats mgcv munsell nlme nnet numDeriv pillar pkgconfig quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo rlang scales tibble timeDate tseries TTR urca utf8 vctrs viridisLite withr xts zoo

Citation

Development and contributors

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