The abc R package provides flexibility for users to implement different ABC algorithms, models, and summary statistics within a single generalist framework. This design addresses the challenge that ABC analysis typically follows a trial-error approach where users experiment with different models and algorithms. [@csillery_abc_2012]
Definitions
Synthesis
The generalist ABC package establishes a flexible architecture by delegating data simulation and summary statistic calculation to users rather than implementing problem-specific simulators, enabling the software to accommodate diverse complex models and adapt to evolving ABC methodologies. This design philosophy mechanistically supports both parameter estimation and model selection workflows by allowing users to define their own simulation procedures while the package handles core algorithmic implementations, including advanced techniques like nonlinear heteroscedastic regression for improved estimation accuracy. The approach creates a clear separation of concerns where inference procedures remain general and reusable across different modeling contexts, with the comparison of observed and simulated data through summary statistics serving as the fundamental bridge between user-defined models and the package’s algorithmic toolkit. While this flexibility clearly enables broader applicability across scientific domains, the notes leave unresolved questions about how to optimally guide users in choosing among available algorithms or determining which summary statistics will yield adequate inferential precision for their specific models.
Related
- User-defined data simulation enables generalist ABC implementation
- Nonlinear heteroscedastic regression improves ABC parameter estimation accuracy
- ABC enables model selection through comparison of observed and simulated data
- ABC bypasses likelihood evaluation through data simulation