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How to Apply These Terms to Your New Programs
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If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
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To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found.
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<one line to give the program's name and a brief idea of what it does.>
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Also add information on how to contact you by electronic and paper mail.
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If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode:
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<program> Copyright (C) <year> <name of author>
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The hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”.
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You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>.
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The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
-
+# mwana: An efficient workflow for plausibility checks and prevalence analysis of wasting in R 
-
-### Prevalence estimation
-
-`mwana` prevalence estimators were built to take decisions on the
-appropriate analysis procedure to follow based on the quality of the
-data, as per the SMART rules. They return output tables with summarized
-results based on the data quality test results. Fundamentally, the
-functions loop over the survey areas in the data set whilst doing
-quality checks and taking decisions on the appropriate prevalence
-analysis path that best fits the data.
-
-`mwana` estimates wasting prevalence on the basis of:
-
-- WFHZ and/or edema. Read the guide
- [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz)
-- Raw MUAC values and/or edema. When variable age is available,
- detection and removal of outliers is based on MFAZ, otherwise based on
- the raw MUAC values. This is simply to exclude outliers; the actual
- prevalence estimation is based on the raw MUAC values. Read the guide
- [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac).
-- MFAZ and/or edema. Read the guide
- [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz).
-- Combined prevalence. A concept of combined flags is used to streamline
- the flags removed in WFHZ and those in MUAC. Read the guide
- [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting).
-
-In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow,
-`mwana` provides a handy function for checking whether the minimum
-sample size requirements of a given area were met, on the basis of the
-methodology used to collect the data, be it a survey, a screening or a
-sentinel site data. Read the guide
-[here](https://nutriverse.io/mwana/articles/ipc_amn_check.html).
-
-> [!TIP]
->
-> If you are undertaking a research and you want to wrangle your data
-> before using it in your statistical models, `mwana` is a great helper.
-
-> [!WARNING]
->
-> Please note that `mwana` is still highly experimental and is
-> undergoing a lot of development. Hence, any functionalities described
-> above have a high likelihood of changing interface or approach as we
-> aim for a stable working version.
+appraisal and prevalence estimation as part of the ***Quality Assurance
+Team*** of the [Integrated Phase Classification
+(IPC)](https://www.ipcinfo.org/) Global Support Unit. The current
+standard child anthropometric data appraisal workflow is extremely
+cumbersome, requiring significant time and effort utilizing different
+software tools - SPSS, Microsoft Excel, [SMART Emergency Nutrition
+Assessment (ENA)
+software](https://smartmethodology.org/survey-planning-tools/smart-emergency-nutrition-assessment/) -
+for each step of the process for a single dataset. This process is
+repeated for every data set needing to be processed and often needing to
+be implemented in a relatively short period of time. This manual and
+repetitive process, by its nature, is extremely error-prone.
+
+`mwana` provides functions that can simplify this cumbersome workflow
+into a process that can be programmatically designed particularly when
+handling multiple-area datasets. Whilst developed with the analytic and
+reporting needs of the IPC Global Support Unit in mind, `mwana` can be
+used generally for anthropometric datasets of children for the purpose
+of assessing data quality and for estimating prevalence of acute
+undernutrition in children 6-59 months old.
## Installation
-`mwana` is not yet on CRAN but can be installed from the [nutriverse R
+`mwana` is not yet on [CRAN](https::cran.r-project.org) but can be
+installed from the [nutriverse R
Universe](https://nutriverse.r-universe.dev) as follows:
``` r
@@ -148,25 +82,105 @@ install.packages(
)
```
-Then load to in memory with
+then loaded into the current environment via
``` r
library(mwana)
```
-# Citation
+## What does `mwana` do?
+
+> [!WARNING]
+>
+> Please note that `mwana` is still experimental but is already in late
+> stage alpha version testing nearing a stable release with development
+> focusing on backwards compatible patch or minor changes. Current
+> functionalities described below may still change in the future but are
+> likely to be compatible with the current interface or approach.
+
+
Currently,
+`mwana` has the following functionalities that support the creation of a
+programmatic workflow illustrated in the figure to the left.
+
+### 1. Data plausibility checks of acute undernutrition anthropometric data of children 6-59 months old
+
+`mwana` has functions for performing data plausibility checks on
+weight-for-height z-score (WFHZ) data based on the SMART plausibility
+checkers, data quality scoring, and data quality classification
+implemented by the ENA for SMART software, their scoring and
+classification criterion. To learn more about these WFHZ plausibility
+checks, the functions that implement them, and how to use these
+function, read this
+[guide](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-wfhz-data).
+
+`mwana` also has functions for performing data plausibility checks on
+mid-upper arm circumference (MUAC) data based on recent research and
+recommendations on MUAC-for-age z-score (MFAZ) and its utility for data
+plausibility checks of MUAC data. To learn more about these MUAC
+plausibility checks, the functions that implement them, and how to use
+these functions, read this
+[guide](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-mfaz-data).
+
+### 2. Prevalence estimation of acute undernutrition
+
+`mwana` has prevalence estimators developed to take into account SMART
+guidelines on estimation approach to use based on an assessment of data
+quality. These functions accept input datasets that include multiple
+survey domains and return summary output tables with prevalence
+estimates for each survey domain.
+
+- To read about the functions and the process for estimating acute
+ undernutrition prevalence from WFHZ and/or edema data, read this
+ [guide](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz).
+
+- To read about the functions and the process for estimating acute
+ undernutrition prevalence from MUAC data, read this
+ [guide](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac)
+ on using raw MUAC and/or edema data and this
+ [guide](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz)
+ on using MFAZ and/or edema data.
+
+- To read about functions and the process for estimating combined acute
+ undernutrition prevalence, read this
+ [guide](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting).
+
+### 3. IPC sample size checker
+
+`mwana` provides a handy function for checking whether a specific
+anthropometric dataset has met the minimum sample size requirements for
+each of the dataset domains based on IPC requirements. The function
+assesses this sample size requirement based on whether the dataset was
+collected through a survey, a screening exercise, or a sentinel site
+surveillance. To learn more about this function, read this
+[guide](https://nutriverse.io/mwana/articles/ipc_amn_check.html).
+
+### 4. Reporting of data plausibility checks and prevalence estimation summary outputs
+
+`mwana` has helper functions that process summary output tables and turn
+them into presentation and/or report ready tables.
+
+> [!TIP]
+>
+> If you are undertaking research using anthropometric data of children
+> 6-59 months old with a focus on acute undernutrition, `mwana` has
+> functions to wrangle ***weight***, ***height***, ***age***,
+> ***WFHZ***, ***MUAC***, and ***MFAZ*** data before using it in your
+> models.
+
+## Citation
-If you were enticed to use `mwana` package and found it useful, please
-cite using the suggested citation provided by a call to `citation`
-function as follows:
+If you use `mwana` package in your work, please cite using the suggested
+citation provided by a call to `citation()` function as follows:
``` r
citation("mwana")
-#> To cite mwana: in publications use:
+#> To cite mwana in publications use:
#>
#> Tomás Zaba, Ernest Guevarra (2024). _mwana: An Efficient Workflow for
-#> Plausibility Checks and Prevalence Analysis of Wasting in R_. R
-#> package version 0.2.0, 
+
+
