An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
Log-linear Poisson autoregression.Journal of Multivariate Analysis, 102(3):563–578
2 Pith papers cite this work, alongside 227 external citations. Polarity classification is still indexing.
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Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.
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glmSTARMA -- An R-Package for fitting autoregressive spatio-temporal models following generalized linear models
An R package that unifies spatio-temporal STARMA and GARCH-type models through double generalized linear models, allowing simultaneous modeling of means and dispersion for count or continuous spatial time series.
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Scalable model selection for count time series with structural breaks: application to solid-organ transplantation during and after COVID-19 in the USA and Italy
Standard count time series models with pandemic break indicators applied to US and Italian transplant data capture COVID deviations, show deceased-donor recovery to baselines, and find auxiliary COVID covariates add negligible predictive value beyond autoregressive and calendar terms.