Package: mwana 0.2.5

Tomás Zaba

mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R

A simple and streamlined workflow for plausibility checks and prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology <https://smartmethodology.org/>, with application in R.

Authors:Tomás Zaba [aut, cre, cph], Ernest Guevarra [aut, cph], Mark Myatt [aut]

mwana_0.2.5.tar.gz
mwana_0.2.5.zip(r-4.7-any)mwana_0.2.5.zip(r-4.6-any)mwana_0.2.5.zip(r-4.5-any)
mwana_0.2.5.tgz(r-4.6-any)mwana_0.2.5.tgz(r-4.5-any)
mwana_0.2.5.tar.gz(r-4.7-any)mwana_0.2.5.tar.gz(r-4.6-any)
mwana_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mwana/json (API)

# Install 'mwana' in R:
install.packages('mwana', repos = c('https://mphimo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mphimo/mwana/issues

Pkgdown/docs site:https://mphimo.github.io

Datasets:
  • anthro.01 - A sample data of district level SMART surveys with location anonymised
  • anthro.02 - A sample of an already wrangled survey data
  • anthro.03 - A sample data of district level SMART surveys conducted in Mozambique
  • anthro.04 - A sample data from a community-based sentinel site with location anonymised
  • mfaz.01 - A sample mid-upper arm circumference (MUAC) screening data
  • mfaz.02 - A sample SMART survey data with mid-upper arm circumference measurements
  • wfhz.01 - A sample SMART survey data with weight-for-height z-score standard deviation rated as problematic

On CRAN:

Conda:

quarto

5.91 score 2 stars 1 packages 6 scripts 403 downloads 24 exports 62 dependencies

Last updated from:c909067631. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK275
source / vignettesOK335
linux-releaseOK228
macos-releaseOK256
macos-oldrelOK293
windows-develOK168
windows-releaseOK194
windows-oldrelOK180
wasm-releaseOK142

Exports:define_wastingflag_outliersget_age_monthsmw_check_ipcamn_ssreqmw_estimate_age_weighted_prev_muacmw_estimate_prevalence_combinedmw_estimate_prevalence_mfazmw_estimate_prevalence_muacmw_estimate_prevalence_screeningmw_estimate_prevalence_screening2mw_estimate_prevalence_wfhzmw_neat_output_mfazmw_neat_output_muacmw_neat_output_wfhzmw_plausibility_check_mfazmw_plausibility_check_muacmw_plausibility_check_wfhzmw_stattest_ageratiomw_stattest_ageratio2mw_wrangle_agemw_wrangle_muacmw_wrangle_wfhzrecode_muacremove_flags

Dependencies:base64encbslibcachemclicommonmarkcpp11DBIdigestdplyrfarverfastmapfontawesomefsgenericsgluehtmltoolshttpuvjquerylibjsonlitelabelinglaterlatticelifecyclelubridatemagrittrMatrixmemoisemimeminqamitoolsnipnTKnumDerivotelpillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcppRcppArmadillorlangsassscalesshinysourcetoolssrvyrstringistringrsurveysurvivaltibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithrxtablezscorer

Checking if IPC Acute Malnutrition sample size requirements were met
References
Rendered fromipc-amn-check.qmdusingquarto::html

Last update: 2026-09-02
Started: 2025-12-10

Running plausibility checks
Introduction | Plausibility check of WFHZ data | Data wrangling | Wrangling age | Wrangling all other remaining variables | On to de facto plausibility check of WFHZ data | Plausibility check of MFAZ data | Age ratio test in MFAZ | Wrangling MFAZ data | On to de facto plausibility check of MFAZ data | Plausibility check of raw MUAC data | On to de facto plausibility check | References
Rendered fromplausibility.qmdusingquarto::html

Last update: 2026-09-02
Started: 2024-10-24

Estimating the prevalence of wasting
Introduction | Estimation of the prevalence of wasting based on WFHZ | Estimation of unweighted prevalence | Estimation of weighted prevalence | The work under the hood of mw_estimate_prevalence_wfhz | Estimation of the prevalence of wasting based on MFAZ | Estimation of the prevalence of wasting based on raw MUAC values | How does it work on a multi-area dataset | Estimation for survey data | Estimation for non-survey data | Estimation of the combined prevalence of wasting | Data wrangling | What is combined flag?
Rendered fromprevalence.qmdusingquarto::html

Last update: 2026-08-15
Started: 2024-10-22

Readme and manuals

Help Manual

Help pageTopics
A sample data of district level SMART surveys with location anonymisedanthro.01
A sample of an already wrangled survey dataanthro.02
A sample data of district level SMART surveys conducted in Mozambiqueanthro.03
A sample data from a community-based sentinel site with location anonymisedanthro.04
Define wastingdefine_wasting
Identify, flag, and remove outliersflag_outliers remove_flags
Calculate child's age in monthsget_age_months
A sample mid-upper arm circumference (MUAC) screening datamfaz.01
A sample SMART survey data with mid-upper arm circumference measurementsmfaz.02
Check whether sample size requirements for IPC Acute Malnutrition (IPC AMN) analysis are metmw_check_ipcamn_ssreq
Estimate age-weighted prevalence of wasting by MUACmw_estimate_age_weighted_prev_muac
Estimate the prevalence of combined wastingmw_estimate_prevalence_combined
Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)mw_estimate_prevalence_mfaz
Estimate the prevalence of wasting based on MUAC for survey datamw_estimate_prevalence_muac
Estimate the prevalence of wasting based on MUAC for non-survey datamw_estimate_prevalence_screening mw_estimate_prevalence_screening2
Estimate the prevalence of wasting based on weight-for-height z-scores (WFHZ)mw_estimate_prevalence_wfhz
Clean and format the output tibble returned from the MUAC-for-age z-score plausibility checkmw_neat_output_mfaz
Clean and format the output tibble returned from the MUAC plausibility checkmw_neat_output_muac
Clean and format the output tibble returned from the WFHZ plausibility checkmw_neat_output_wfhz
Check the plausibility and acceptability of MUAC-for-age z-score (MFAZ) datamw_plausibility_check_mfaz
Check the plausibility and acceptability of raw MUAC datamw_plausibility_check_muac
Check the plausibility and acceptability of weight-for-height z-score (WFHZ) datamw_plausibility_check_wfhz
Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months oldmw_stattest_ageratio mw_stattest_ageratio2
Wrangle child's agemw_wrangle_age
Wrangle MUAC datamw_wrangle_muac
Wrangle weight-for-height datamw_wrangle_wfhz
Convert MUAC values to either centimetres or millimetresrecode_muac
A sample SMART survey data with weight-for-height z-score standard deviation rated as problematicwfhz.01