| Countr-package {Countr} | R Documentation |
Flexible univariate count models based on renewal processes. The models may include covariates and can be specified with familiar formula syntax as in glm() and 'flexsurv'.
The methodology is described in the forthcoming paper
(Kharrat et al. 2019)
in the Journal of Statistical Software (included in the package as vignette
vignette('Countr_guide_paper', package = "Countr")).
The main function is renewalCount, see its documentation for
examples.
Goodness of fit chi-square (likelihood ratio and Pearson) tests for glm and
count renewal models are implemented in chiSq_gof and
chiSq_pearson.
Kharrat T, Boshnakov GN, McHale I, Baker R (2019). “Flexible Regression Models for Count Data Based on Renewal Processes: The Countr Package.” Journal of Statistical Software, 90(13), 1–35. doi:10.18637/jss.v090.i13.
Baker R, Kharrat T (2017). “Event count distributions from renewal processes: fast computation.” IMA Journal of Management Mathematics.
Boshnakov G, Kharrat T, McHale IG (2017). “A bivariate Weibull count model for forecasting association football scores.” International Journal of Forecasting, 33(2), 458–466.
Cameron AC, Trivedi PK (2013). Regression analysis of count data, volume 53. Cambridge university press.
Kharrat T, Boshnakov GN, McHale IG, Baker R (2018). “Flexible regression models for count data based on renewal processes: the Countr package.” Journal of Statistical Software (to appear).
McShane B, Adrian M, Bradlow ET, Fader PS (2008). “Count models based on Weibull interarrival times.” Journal of Business & Economic Statistics, 26(3), 369–378.
Winkelmann R (1995). “Duration dependence and dispersion in count-data models.” Journal of Business & Economic Statistics, 13(4), 467–474.