URL: https://sima-njf.github.io/epiworldRcalibrate/,
        https://github.com/sima-njf/epiworldRcalibrate
BugReports: https://github.com/sima-njf/epiworldRcalibrate/issues
Package: epiworldRcalibrate
Title: Fast and Effortless Calibration of Agent-Based Models using
        Machine Learning
Version: 0.1.2
Authors@R: c(
  person(
    "Sima", "Najafzadehkhoei", role=c("aut","cre"), email="sima.njf@utah.edu",
    comment = c(ORCID = "0009-0002-6253-2910")
  ),
  person(
    "George", "Vega Yon", role=c("aut"), email="g.vegayon@gmail.com",
    comment = c(ORCID = "0000-0002-3171-0844")
  ),
  person(
    "Bernardo", "Modenesi", role=c("aut"), email="bmodenesi@gmail.com"
  ),
  person("Centers for Disease Control and Prevention", role = "fnd",
         comment = "Award number 1U01CK000585; 75D30121F00003")
  )
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) <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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Suggests: testthat (>= 3.0.0), epiworldR
Config/testthat/edition: 3
Imports: reticulate (>= 1.2), utils
Depends: R (>= 3.5)
LazyData: true
NeedsCompilation: no
Packaged: 2026-02-13 20:07:22 UTC; u1418987
Author: Sima Najafzadehkhoei [aut, cre] (ORCID:
    <https://orcid.org/0009-0002-6253-2910>),
  George Vega Yon [aut] (ORCID: <https://orcid.org/0000-0002-3171-0844>),
  Bernardo Modenesi [aut],
  Centers for Disease Control and Prevention [fnd] (Award number
    1U01CK000585; 75D30121F00003)
Maintainer: Sima Najafzadehkhoei <sima.njf@utah.edu>
Repository: CRAN
Date/Publication: 2026-02-18 18:00:02 UTC
Built: R 4.4.3; ; 2026-03-02 17:10:05 UTC; windows
