Package index • mwana

Description

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

Wrangle data

mw_wrangle_age(): Wrangle child's age

mw_wrangle_wfhz(): Wrangle weight-for-height data

mw_wrangle_muac(): Wrangle MUAC data

Statistical tests

mw_stattest_ageratio() mw_stattest_ageratio2(): Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old

IPC sample size checks

mw_check_ipcamn_ssreq(): Check whether sample size requirements for IPC Acute Malnutrition (IPC AMN) analysis are met

Plausibility checks

mw_plausibility_check_wfhz(): Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data

mw_plausibility_check_mfaz(): Check the plausibility and acceptability of MUAC-for-age z-score (MFAZ) data

mw_plausibility_check_muac(): Check the plausibility and acceptability of raw MUAC data

Neat output tables

mw_neat_output_wfhz(): Clean and format the output tibble returned from the WFHZ plausibility check

mw_neat_output_mfaz(): Clean and format the output tibble returned from the MUAC-for-age z-score plausibility check

mw_neat_output_muac(): Clean and format the output tibble returned from the MUAC plausibility check

Prevalence estimators

mw_estimate_prevalence_wfhz(): Estimate the prevalence of wasting based on weight-for-height z-scores (WFHZ)

mw_estimate_prevalence_muac() mw_estimate_smart_age_wt(): Estimate the prevalence of wasting based on MUAC for survey data

mw_estimate_prevalence_mfaz(): Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)

mw_estimate_prevalence_combined(): Estimate the prevalence of combined wasting

mw_estimate_prevalence_screening() mw_estimate_prevalence_screening2(): Estimate the prevalence of wasting based on MUAC for non-survey data

Utilities

get_age_months(): Calculate child's age in months

recode_muac(): Convert MUAC values to either centimeters or millimeters

flag_outliers() remove_flags(): Identify, flag, and remove outliers

define_wasting(): Define wasting

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 in an anonymized location

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