CRAN/E | coconots

coconots

Convolution-Closed Models for Count Time Series

Installation

About

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 modeled 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.

Key Metrics

Version 1.1.3
R ≥ 3.5.0
Published 2023-10-01 214 days ago
Needs compilation? yes
License MIT
License File
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Maintainer

Maintainer

Manuel Huth

manuel.huth@yahoo.com

Authors

Manuel Huth

aut / cre

Robert C. Jung

aut

Andy Tremayne

aut

Material

README
Reference manual
Package source

In Views

TimeSeries

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

coconots archive

Depends

R ≥ 3.5.0

Imports

Rcpp
forecast
numDeriv
HMMpa
stats
ggplot2
utils
matrixStats
JuliaConnectoR

Suggests

covr
testthat ≥ 3.0.0

LinkingTo

Rcpp
StanHeaders ≥ 2.21.0
RcppParallel ≥ 5.0.1