epiworldRcalibrate (version 0.1.4)

epiworldRcalibrate-package: epiworldRcalibrate: Fast and Effortless Calibration of Agent-Based Models using Machine Learning

Description

Provides tools and pre-trained Machine Learning [ML] models for calibration of Agent-Based Models [ABMs] built with the R package 'epiworldR'. Implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer (2025) tools:::Rd_expr_doi("10.48550/arXiv.2509.07013"). Users can automatically calibrate ABMs in seconds with pre-trained ML models, effectively focusing on simulation rather than calibration. Bridges a gap by allowing public health practitioners to run their own ABMs without the advanced technical expertise often required by calibration.

Author

Maintainer: Sima Najafzadehkhoei (ORCID)

Authors:

• George Vega Yon (ORCID)

• Jake Wagoner

• Bernardo Modenesi

Other contributors:

• Centers for Disease Control and Prevention (Award number 1U01CK000585; 75D30121F00003) [funder]

See Also

Useful links:

• https://sima-njf.github.io/epiworldRcalibrate/

• https://github.com/sima-njf/epiworldRcalibrate

• Report bugs at https://github.com/sima-njf/epiworldRcalibrate/issues