GWRM

GWRM implements the generalized Waring regression model to analyze count data by explicitly modeling three sources of variability: randomness, liability, and proneness.

Key Features:

• Model Fitting: Fits the generalized Waring regression model to count data, accommodating randomness, liability, and proneness.
• Model Description and Validation: Provides tools to describe fitted models and validate their assumptions and performance.
• Non-GLM Model: The generalized Waring regression model does not belong to the family of generalized linear models, unlike negative binomial regression.
• Methods on Fitted Objects: Includes modeling functions and methods that operate on fitted model objects.
• R Integration: Integrates into the R ecosystem for modeling count data.

Scientific Applications:

• Health sciences: Analyzes count data in health-related studies where variability arises from multiple sources.
• Sports science: Models count outcomes in sports research that exhibit complex variability patterns.

Methodology:

Fits the generalized Waring regression model to count data by incorporating randomness, liability, and proneness; provides functions for model description and validation and methods that operate on fitted model objects, and the model does not belong to the generalized linear model family.