Fast and Effortless Calibration of Agent-Based Models using Machine Learning R package epiworldRcalibrate version 0.1.4 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. Software Depends: R (>= 3.5) Imports: reticulate (>= 1.41), utils Suggests: testthat (>= 3.0.0), epiworldR Sima Najafzadehkhoei <sima.njf@utah.edu> Comprehensive R Archive Network (CRAN) Sima Najafzadehkhoei [aut, cre] (ORCID: <https://orcid.org/0009-0002-6253-2910>), George Vega Yon [aut] (ORCID: <https://orcid.org/0000-0002-3171-0844>), Jake Wagoner [aut], Bernardo Modenesi [aut], Centers for Disease Control and Prevention [fnd] (Award number 1U01CK000585; 75D30121F00003) MIT + file LICENSE (https://CRAN.R-project.org/package=epiworldRcalibrate/LICENSE) 2026-04-02 application/tgz https://CRAN.R-project.org/package=epiworldRcalibrate doi:10.32614/CRAN.package.epiworldRcalibrate