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

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) <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.

Version:	0.1.2
Depends:	R (≥ 3.5)
Imports:	reticulate (≥ 1.2), utils
Suggests:	testthat (≥ 3.0.0), epiworldR
Published:	2026-02-18
DOI:	10.32614/CRAN.package.epiworldRcalibrate (may not be active yet)
Author:	Sima Najafzadehkhoei [aut, cre], George Vega Yon [aut], Bernardo Modenesi [aut], Centers for Disease Control and Prevention [fnd] (Award number 1U01CK000585; 75D30121F00003)
Maintainer:	Sima Najafzadehkhoei <sima.njf at utah.edu>
BugReports:	https://github.com/sima-njf/epiworldRcalibrate/issues
License:	MIT + file LICENSE
URL:	https://sima-njf.github.io/epiworldRcalibrate/, https://github.com/sima-njf/epiworldRcalibrate
NeedsCompilation:	no
Citation:	epiworldRcalibrate citation info
Materials:	README, NEWS
CRAN checks:	epiworldRcalibrate results

Documentation:

Reference manual:

epiworldRcalibrate.html , epiworldRcalibrate.pdf

Downloads:

Package source:	epiworldRcalibrate_0.1.2.tar.gz
Windows binaries:	r-devel: epiworldRcalibrate_0.1.2.zip, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): epiworldRcalibrate_0.1.2.tgz, r-oldrel (arm64): epiworldRcalibrate_0.1.2.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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