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.
