Package index • mwana

All functions

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 of a community-based sentinel site from an anonymized location

apply_cdc_age_weighting(): Apply the CDC/SMART prevalence weighting approach on MUAC data

define_wasting_cases_muac() define_wasting_cases_whz() define_wasting_cases_combined() define_wasting(): Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria

classify_wasting_for_cdc_approach(): Classify wasting into severe or moderate wasting to be used in the SMART MUAC tool weighting approach

compute_pps_based_combined_prevalence() compute_combined_prevalence(): Compute combined prevalence of wasting

compute_weighted_prevalence(): Apply the CDC/SMART prevalence weighting approach on MUAC data

get_age_months(): Calculate child's age in months

mfaz.01: A sample MUAC screening data from an anonymized setting

mfaz.02: A sample SMART survey data with MUAC

mw_check_ipcamn_ssreq(): Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met

mw_neat_output_mfaz(): Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability

mw_neat_output_muac(): Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability.

mw_neat_output_wfhz(): Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability

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

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

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

mw_wrangle_age(): Wrangle child's age

mw_wrangle_muac(): Wrangle MUAC data

mw_wrangle_wfhz(): Wrangle weight-for-height data

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

compute_mfaz_prevalence() compute_muac_prevalence() compute_wfhz_prevalence(): Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC

apply_probit_approach() compute_probit_prevalence(): Compute the prevalence estimates of wasting on the basis of the PROBIT method.

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

tell_muac_analysis_strategy(): A helper function to determine the MUAC prevalence analysis approach to follow

wfhz.01: A sample SMART survey data with WFHZ standard deviation rated as problematic