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Analysis of ODE models with focus on model selection and parameter estimation.

Project Description

S-timator is a Python library to analyse ODE-based models (also known as dynamic or kinetic models). These models are often found in many scientific fields, particularly in Physics, Chemistry, Biology and Engineering.

Features include:

  • A mini language used to describe models: models can be input as plain text following a very simple and human-readable language.
  • Basic analysis: numerical solution of ODE’s, parameter scanning.
  • Parameter estimation and model selection: given experimental data in the form of time series and constrains on model operating ranges, built-in numerical optimizers can find parameter values and assist you in the experimental design for model selection.

S-timator is in an alpha stage: many new features will be available soon.


S-timator supports Python versions 2.7 and 3.3+.

S-timator depends on the “scientific python stack”. The mandatory requirements for S-timator are the following libraries:

  • Python (2.7 or 3.3+)
  • numpy
  • scipy
  • matplotlib
  • pip

One of the following “scientific python” distributions is recommended, as they all provide an easy installation of all requirements:

The installation of these Python libraries is optional, but strongly recommended:

  • sympy: necessary to compute dynamic sensitivities, error estimates of parameters and other symbolic computations.
  • Jupyter and all its dependencies: some S-timator examples are provided as Jupyter notebooks.


After installing the required libraries, (Python, numpy, scipy, matplotlib) the easiest way to install S-timator is with pip:

$ pip install stimator

or, in a Anaconda/Miniconda installation, install from the aeferreira channel:

$ conda install -c aeferreira stimator

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