Merge pull request #97 from nutriverse/dev

Release to main

DESCRIPTION

@@ -1,18 +1,17 @@
 Type: Package
 Package: mwana
-Title: Utilities for Analysing Children's Nutritional Status 
-Version: 0.1.0.9000
+Title: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R
+Version: 0.2.0.9000
 Authors@R: c(
     person("Tomás", "Zaba", , "[email protected]", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "0000-0002-7079-3574")),
     person("Ernest", "Guevarra", role = c("aut", "cph"),
            comment = c(ORCID = "0000-0002-4887-4415"))
   )
-Description: A streamlined and comprehensive implementation of the Standardized
+Description: 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/> guidelines for data quality checks and 
-  prevalence estimation, with enhanced programmable process particularly when 
-  handling multiple area datasets.
+  <https://smartmethodology.org/>, with application in R.
 License: GPL (>= 3)
 URL: https://github.com/nutriverse/mwana, https://nutriverse.io/mwana
 Imports: 

NAMESPACE

@@ -1,12 +1,15 @@
 # Generated by roxygen2: do not edit by hand
 
-export(compute_combined_prevalence)
-export(compute_muac_prevalence)
-export(compute_wfhz_prevalence)
 export(define_wasting)
 export(flag_outliers)
 export(get_age_months)
 export(mw_check_ipcamn_ssreq)
+export(mw_estimate_prevalence_combined)
+export(mw_estimate_prevalence_mfaz)
+export(mw_estimate_prevalence_muac)
+export(mw_estimate_prevalence_screening)
+export(mw_estimate_prevalence_wfhz)
+export(mw_estimate_smart_age_wt)
 export(mw_neat_output_mfaz)
 export(mw_neat_output_muac)
 export(mw_neat_output_wfhz)
@@ -20,15 +23,20 @@ export(mw_wrangle_wfhz)
 export(recode_muac)
 export(remove_flags)
 importFrom(dplyr,across)
+importFrom(dplyr,bind_rows)
 importFrom(dplyr,case_when)
 importFrom(dplyr,ends_with)
 importFrom(dplyr,everything)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
+importFrom(dplyr,is_grouped_df)
 importFrom(dplyr,mutate)
 importFrom(dplyr,n)
 importFrom(dplyr,n_distinct)
+importFrom(dplyr,pull)
+importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
+importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(lubridate,ymd)
 importFrom(methods,is)
@@ -40,6 +48,8 @@ importFrom(nipnTK,skewKurt)
 importFrom(rlang,.data)
 importFrom(rlang,enquo)
 importFrom(rlang,eval_tidy)
+importFrom(rlang,quo_is_null)
+importFrom(rlang,quo_name)
 importFrom(rlang,sym)
 importFrom(scales,label_percent)
 importFrom(scales,label_pvalue)
@@ -51,4 +61,5 @@ importFrom(stats,prop.test)
 importFrom(stats,sd)
 importFrom(stats,setNames)
 importFrom(tibble,as_tibble)
+importFrom(tibble,tibble)
 importFrom(zscorer,addWGSR)

NEWS.md

@@ -1,7 +1,28 @@
-# mwana v0.1.0.9000 (development version)
+# mwana v0.2.0.9000 (development version)
 
 <br/>
 
+# mwana v0.2.0
+
+## New features
+
+* Added new function `mw_estimate_prevalence_screening()` to estimate prevalence 
+of wasting by MUAC from non survey data: screenings, sentinel sites, etc.
+
+## Bug fixes
+
+* Resolved issues with `mw_neat_output_mfaz()`, `mw_neat_output_wfhz()` and 
+`mw_neat_output_muac()` not returning neat and tidy output for grouped `data.frame` 
+from their respective plausibility checkers.
+
+* Resolved issue with `edema` argument in prevalence functions that was not working
+as expected when set to `NULL`.
+
+## General updates
+
+* Updated general package documentation, including references in vignettes. 
+* Built package using `R` version 4.4.2
+
 # mwana v0.1.0
 
 * Initial pre-release version for alpha-testing.

R/data.R

@@ -74,13 +74,13 @@
 #' A sample data of district level SMART surveys conducted in Mozambique
 #'
 #' @description
-#' `anthro.03` contains survey data of four districts. Each district dataset
+#' `anthro.03` contains survey data of four districts. Each district data set
 #' presents distinct data quality scenarios that requires tailored prevalence
 #' analysis approach: two districts show a problematic WFHZ standard deviation
 #' whilst the remaining are all within range.
 #'
 #' This sample data is useful to demonstrate the use of the prevalence functions on
-#' a multi-area survey data where there can be variations in the rating of
+#' a multiple-area survey data where there can be variations in the rating of
 #' acceptability of the standard deviation, hence require different analyses approaches
 #' for each area to ensure accurate estimation.
 #'
@@ -113,7 +113,7 @@
 #'
 #' @description
 #' Data was generated through a community-based sentinel site conducted
-#' across three provinces. Each province's dataset presents distinct
+#' across three provinces. Each province's data set presents distinct
 #' data quality scenarios, requiring tailored prevalence analysis:
 #'  + "Province 1" has MFAZ's standard deviation and age ratio test rating of
 #'  acceptability falling within range;
@@ -122,7 +122,7 @@
 #'  + "Province 3" has both tests rated as problematic.
 #'
 #' This sample data is useful to demonstrate the use of prevalence functions on
-#' a multi-area survey data where variations in the rating of acceptability of the
+#' a multiple-area survey data where variations in the rating of acceptability of the
 #' standard deviation exist, hence require different analyses approaches for each
 #' area to ensure accurate estimation.
 #'