setJuliaSeed

Sets the seed for julia's random number generator to ensure reproducibility.

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>.

Manuel Huth

coconots

Convolution-Closed Models for Count Time Series

Robert C. Jung

Andy Tremayne

setJuliaSeed function

<dl><dt>julia_seed</dt>
<dd>An integer seed value to be passed to julia's random number generator.</dd></dl>

Arguments

Author

Set Seed for julia's Random Number Generator — setJuliaSeed

<dl>

<dt>julia_seed</dt>
<dd>An integer seed value to be passed to julia's random number generator.</dd>

</dl>

setJuliaSeed: Set Seed for julia's Random Number Generator

Description

Usage

Arguments

Author

Details