Abstract
A new alternative to the standard Poisson regression model for count data is
suggested. This new family of models is based on discrete distributions
derived from renewal processes, i.e. distributions of the number of events by
some time t. Unlike the Poisson model, these models have, in general,
time-dependent hazard functions. Any survival distribution can be used to
describe the inter-arrival times between events, which gives a rich class of
count processes with great flexibility for modelling both underdispersed and
overdispersed data. The R package Countr provides a function, renewalCount(),
for fitting renewal count regression models and methods for working with the
fitted models. The interface is designed to mimic the glm() interface
and standard methods for model exploration, diagnosis and prediction are
implemented. Package Countr implements state-of-the-art recently
developed methods for fast computation of the count probabilities.
The package functionalities are illustrated using several datasets.
suggested. This new family of models is based on discrete distributions
derived from renewal processes, i.e. distributions of the number of events by
some time t. Unlike the Poisson model, these models have, in general,
time-dependent hazard functions. Any survival distribution can be used to
describe the inter-arrival times between events, which gives a rich class of
count processes with great flexibility for modelling both underdispersed and
overdispersed data. The R package Countr provides a function, renewalCount(),
for fitting renewal count regression models and methods for working with the
fitted models. The interface is designed to mimic the glm() interface
and standard methods for model exploration, diagnosis and prediction are
implemented. Package Countr implements state-of-the-art recently
developed methods for fast computation of the count probabilities.
The package functionalities are illustrated using several datasets.
Original language | English |
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Pages (from-to) | 0 |
Journal | Journal of Statistical Software |
Volume | 90 |
Issue number | 13 |
DOIs | |
Publication status | Published - 21 Aug 2019 |