Package: SurveyNCD
Title: Tools for Self-Reported Health Indicators in Complex Surveys
Version: 0.1.0
Authors@R: 
    person("Sujon", "Mia", email = "sujonsgc@gmail.com",
           role = c("aut", "cre"))
Description: Provides tools for analysing population health survey data
        (WHO STEPS, DHS, MICS, and similar complex sample surveys),
        where chronic conditions are self-reported rather than
        ICD-coded and estimates must account for stratification,
        clustering, and sampling weights. Includes a self-reported
        multimorbidity index based on the Functional Comorbidity Index
        (Groll et al., 2005, <doi:10.1016/j.jclinepi.2004.10.018>),
        design-weighted population prevalence estimation via the
        'survey' package, a survey-weighted concentration index for
        health inequality analysis, a DHS anthropometric z-score
        categoriser, a choropleth mapping helper, and exploratory
        survey-weighted gradient boosting (via 'xgboost') with
        SHAP-based explainability. The gradient boosting component
        applies case weights but does not yet propagate cluster and
        strata design effects into variance estimates; it should be
        treated as exploratory rather than as design-based inference.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
VignetteBuilder: knitr
Imports: dplyr, ggplot2, magrittr, rlang, stats, survey, tidyr
Suggests: knitr, rmarkdown, sf, srvyr, testthat (>= 3.0.0), xgboost
Config/testthat/edition: 3
URL: https://github.com/StatAid-Research-Lab/SurveyNCD
BugReports: https://github.com/StatAid-Research-Lab/SurveyNCD/issues
Config/roxygen2/version: 8.0.0
Config/pak/sysreqs: make libicu-dev
Repository: https://stataid-research-lab.r-universe.dev
Date/Publication: 2026-10-16 18:30:03 UTC
RemoteUrl: https://github.com/stataid-research-lab/surveyncd
RemoteRef: HEAD
RemoteSha: 94fd2415513c4c9a44b66eae33ebd93cc5212d35
NeedsCompilation: no
Packaged: 2026-10-22 11:58:21 UTC; root
Author: Sujon Mia [aut, cre]
Maintainer: Sujon Mia <sujonsgc@gmail.com>