Convolution-Closed Models for Count Time Series
Bootstrap Based Model Assessment Procedure
coconots: Convolution-Closed Models for Count 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
Computes Scores for Various Models Maintaining a Common Sample
installJuliaPackages
K-Step Ahead Forecast Distributions
Objects exported from other packages
Set Seed for '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 modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through 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> and, Tsay (1992) <doi:10.2307/2347612>.