Convolution-Closed Models for Count Time Series
Bootstrap Based Model Assessment Procedure
Concolution-closed Models for Time Series
Probability Integral Transform Based Model Assessment Procedure
cocoReg
Residual Based Model Assessment Procedure
Scoring Rule Based Model Assessment Procedure
Simulation of Count Time Series
installJuliaPackages
K-Step Ahead Forecast Bootstrapping
Objects exported from other packages
Set Seed for Julia's Random Number Generator
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>.