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vacalibration: Calibration of Computer-Coded Verbal Autopsy Algorithm

Calibrates population-level cause-specific mortality fractions (CSMFs) that are derived using computer-coded verbal autopsy (CCVA) algorithms. Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;<https://champshealth.org/>) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;<doi:10.1214/24-AOAS2006>) and are analyzed in Pramanik et al. (2026;<doi:10.1136/bmjgh-2025-021747>). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;<doi:10.1214/24-AOAS2006>) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute.

Version: 2.2
Depends: R (≥ 3.5)
Imports: rstan, openVA, parallel, ggplot2, patchwork, reshape2, LaplacesDemon, MASS
Suggests: knitr, rmarkdown
Published: 2026-03-20
DOI: 10.32614/CRAN.package.vacalibration
Author: Sandipan Pramanik ORCID iD [aut, cre], Emily Wilson [aut], Jacob Fiksel [aut], Brian Gilbert [aut], Abhirup Datta [aut]
Maintainer: Sandipan Pramanik <sandy.pramanik at gmail.com>
BugReports: https://github.com/sandy-pramanik/vacalibration/issues
License: MIT + file LICENSE
URL: https://github.com/sandy-pramanik/vacalibration
NeedsCompilation: no
Materials: README
CRAN checks: vacalibration results

Documentation:

Reference manual: vacalibration.html , vacalibration.pdf
Vignettes: Intro to vacalibration (source, R code)

Downloads:

Package source: vacalibration_2.2.tar.gz
Windows binaries: r-devel: vacalibration_2.2.zip, r-release: vacalibration_2.2.zip, r-oldrel: vacalibration_2.2.zip
macOS binaries: r-release (arm64): vacalibration_2.2.tgz, r-oldrel (arm64): vacalibration_2.2.tgz, r-release (x86_64): vacalibration_2.2.tgz, r-oldrel (x86_64): vacalibration_2.2.tgz
Old sources: vacalibration archive

Reverse dependencies:

Reverse suggests: openVA

Linking:

Please use the canonical form https://CRAN.R-project.org/package=vacalibration to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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