<!-- badges: start -->
[![Codecov test coverage](https://codecov.io/gh/LeidenAdvancedPKPD/amp.dm/graph/badge.svg)](https://app.codecov.io/gh/LeidenAdvancedPKPD/amp.dm)
<!-- badges: end -->

# Introduction <img src="man/figures/logo.png" align="right" alt="" width="120" />

The aim of this package is to ease or **amp**lify the process of creating datasets mainly for pharmacometric analyses. 
The package contains functions to create NONMEM specific variables, perform various time calculations and 
help in creating dosing records. Finally, multiple functions are present for documentation and QC purposes.

The latest development version can be installed using:

`devtools::install_github("LeidenAdvancedPKPD/amp.dm")`

The package is also available on CRAN and can be installed using:

`install.packages("amp.dm")`

The article section should get you started. Within this section there is also a PDF that 
represents a study example, and include code for the main functionality of the package. 
This [example](https://github.com/LeidenAdvancedPKPD/amp.dm/blob/main/vignettes/getting_started.qmd) can also be used as a starting point or template for new studies.

This package was initially developed as an in-house package at LAP&P, and was started in 2015. Various versions were developed, where many people within
LAP&P helped in making the package better and more robust. Without them this package wouldn't be possible!  

# Other packages

There are many packages for general data management like the entire `tidyverse` collection and more specific for pharma the `pharmaverse` collection.

This package is more specific for pharmacometric datasets which require some specific functionality. 
Related to these type of datasets there is the [`NMdata`](https://nmautoverse.github.io/NMdata/) package.
The main difference with this package is that `NMdata` combines data management tasks with processing of NONMEM results.
This package is more focused towards dataset creation, including handling meta data and documentation.

The [`yspec`](https://metrumresearchgroup.github.io/yspec/) and [`yamlet`](https://CRAN.R-project.org/package=yamlet)
packages are mainly build for handling meta data, while this package handles meta data differently and 
combines this in the 'documented' workflow.
