Package: vacalibration
Title: Calibration of Computer-Coded Verbal Autopsy Algorithm
Version: 2.0
Authors@R: c(person("Sandipan", "Pramanik", role = c("aut", "cre"),
                    email = "sandy.pramanik@gmail.com",
                    comment = c(ORCID = "0000-0002-7196-155X")),
             person("Emily", "Wilson", role = "aut",
                    email = "wilsonem@gmail.com"),
             person("Jacob", "Fiksel", role = "aut",
                    email = "jfiksel@gmail.com"),
             person("Brian", "Gilbert", role = "aut",
                    email = "bgilbert345@gmail.com"),
             person("Abhirup", "Datta", role = "aut",
                    email = "abhidatta@jhu.edu"))
Maintainer: Sandipan Pramanik <sandy.pramanik@gmail.com>
Description: Calibrates cause-specific mortality fractions (CSMF) estimates generated by computer-coded verbal autopsy (CCVA) algorithms from WHO-standardized verbal autopsy (VA) survey data. It leverages data from the multi-country Child Health and Mortality Prevention Surveillance (CHAMPS) project <https://champshealth.org/>, which determines gold standard causes of death via Minimally Invasive Tissue Sampling (MITS). By modeling the CHAMPS data using the misclassification matrix modeling framework proposed in Pramanik et al. (2025, <doi:10.1214/24-AOAS2006>), the package includes an inventory of 48 uncertainty-quantified misclassification matrices for three CCVA algorithms (EAVA, InSilicoVA, InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight "countries" (seven countries in CHAMPS -- Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa -- and an estimate for countries not in CHAMPS). Given a VA-only data for an age group, CCVA algorithm, and country, the package uses the corresponding uncertainty-quantified misclassification matrix estimates as an informative prior, and utilizes the modular VA-calibration to produce calibrated CSMF estimates. It also supports ensemble calibration when VA-only data are provided for multiple algorithms. More generally, the package can be applied to calibrate predictions from a discrete classifier (or ensemble of classifiers) utilizing user-provided fixed or uncertainty-quantified misclassification matrices. This work is supported by the Bill and Melinda Gates Foundation Grant INV-034842.
License: GPL-2
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: rstan, ggplot2, loo, patchwork, reshape2
Config/testthat/edition: 3
Depends: R (>= 3.5)
LazyData: true
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-07-23 01:14:51 UTC; sandipanpramanik
Author: Sandipan Pramanik [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-7196-155X>),
  Emily Wilson [aut],
  Jacob Fiksel [aut],
  Brian Gilbert [aut],
  Abhirup Datta [aut]
Repository: CRAN
Date/Publication: 2025-07-24 12:50:02 UTC
Built: R 4.6.0; ; 2025-12-17 05:04:58 UTC; windows
