Pharmpy is an open-source software package for pharmacometric modeling. It has functionality ranging from reading and manipulating model files and datasets to full tools where subsequent results are collected and presented.
Features include:
- A model abstraction which splits a model into core components which Pharmpy understands and can manipulate: parameters, random variables, statements (including ODE system), dataset, and execution steps
- An abstraction for modelfit results which splits a parsed results into core components: e.g. OFV, parameter estimates, relative standard errors (RSEs), residuals, predictions
- Functions for manipulation of models and datasets in the modeling-module: e.g. change structural model, add time-after-dose column, deidentify dataset
- Tools to aid model development in the tools-module: execution of models within Python/R scripts, automatic development of models (e.g. AMD, IIVSearch, RUVSearch), comparison of estimation methods
- Support for multiple estimation tools: parse NONMEM models, execute NONMEM, nlmixr2, and rxODE2 models, run all Pharmpy tools with NONMEM and some with nlmixr2
For more comprehensive information and documentation, see: https://pharmpy.github.io
Pharmpy can be used as a regular Python package, in R via the pharmr package, or via its built in command line interface.
The sections below are intended as first steps, please check our website website for more comprehensive documentation, such as user guides and API references.
For installation in R, see pharmr.
Install the latest stable version from PyPI:
pip install pharmpy-core # or 'pip3 install' if that is your default python3 pip
>>> from pharmpy.modeling import read_model
>>> from pharmpy.tools import load_example_modelfit_results
>>> model = load_example_model("pheno")
>>> model.parameters
value lower upper fix
POP_CL 0.004693 0.00 ∞ False
POP_VC 1.009160 0.00 ∞ False
COVAPGR 0.100000 -0.99 ∞ False
IIV_CL 0.030963 0.00 ∞ False
IIV_VC 0.031128 0.00 ∞ False
SIGMA 0.013086 0.00 ∞ False
>>> res = load_example_modelfit_results("pheno")
>>> res.parameter_estimates
POP_CL 0.004696
POP_VC 0.984258
COVAPGR 0.158920
IIV_CL 0.029351
IIV_VC 0.027906
SIGMA 0.013241
Name: estimates, dtype: float64
>>>
# Get help
pharmpy -h
# Remove first ID from dataset and save new model using new dataset
pharmpy data filter run1.mod 'ID!=1'
# Run tool for selecting IIV structure
pharmpy run iivsearch run1.mod
This is the team behind Pharmpy
Please ask a question in an issue or contact one of the maintainers if you have any questions.
If you interested in contributing to Pharmpy, you can find more information under Contribute.
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