good: An R package for modelling count data
Abstract Organisms‐related data often appear as counts. The Poisson distribution is the most popular choice for modelling count data, but this distribution assumes equidispersion, which is usually not satisfied in real‐world data. Deviations from the Poisson assumption lead to discrete‐valued distri...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14387 |
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| Summary: | Abstract Organisms‐related data often appear as counts. The Poisson distribution is the most popular choice for modelling count data, but this distribution assumes equidispersion, which is usually not satisfied in real‐world data. Deviations from the Poisson assumption lead to discrete‐valued distributions that can fit over‐ and/or underdispersion. Although models for count data with over‐dispersion have been widely considered in the literature, models for underdispersion—the opposite phenomenon—have received less attention because underdispersion is relatively common only in certain research fields, including ecology. The Good distribution is a flexible option for modelling count data with over‐dispersion or underdispersion, although no R packages are available so far offering functionalities such as calculating quantiles, probabilities, etc., of a Good distribution or providing a method for modelling a Good‐distributed output based on a number of potential predictors. This paper presents the R package good, which computes the standard probabilistic functions, generates random samples from a population following a Good distribution and estimates the Good regression. |
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| ISSN: | 2041-210X |