Pharmacometrics and PK/PD modeling offers unique information for decision-making in several steps of drug development. However, the leg work in pharmacometrics remains technical, and this is a typical bottleneck for a pharmacometrician to contributing even more.
Creating datasets - and if you use Nonmem, reading the results data - can be tedious, and mistakes can lead to hours of frustration. This package provides useful tools (including automated checks) for these trivial tasks. The aim is to automate the book keeping and allow more time for the actual analysis.
A central design feature of NMdata is that all included tools require as little as possible about how the user works. Any functionality in the package can be used independently of the rest of the package, and NMdata is not intended to force you to change any habits or preferences. Instead, NMdata tries to fit in with how you (or your colleague who worked on the project before you) do things.
The data set creation tools in NMdata may be equally interesting to users of nlmixr or other proprietary software than Nonmem. However, so far, the most elaborated features of the package relates to the final organization and writing of data for Nonmem, and reading data from Nonmem after a model run.
The best place to browse information about the package is here. All documentation is of course included in the package itself too.
Reading the resulting data from Nonmem can require a few
manual steps. Especially because all modelers seem to do things a
NMscanData can return all data output (
$TABLE) from Nonmem combined, and if wanted with additional columns and rows in
input data. It's as simple as
results <- NMscanData("run001.lst")
Take a look at this vignette for more info on the Nonmem data reader.
On the data-generation side, functionality is provided for documentation of the datasets while generating them. Check out this vignette on the topic. There are functions for automatic checks of (some) data merges, handling and counting of exclusions flags, final preparations for ensuring readability in Nonmem, and ensuring traceability of datasets back to data generation scripts.
NMdata is aimed at CRAN release in near future. Meanwhile, installing
from Github is easy:
library(remotes) install_github("philipdelff/NMdata") library(NMdata)
See the FAQ for how to install specific releases from Github (ensuring reproducibility).
The best way to request features, report bugs etc. is by the github page.
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