myFitter is hosted by Hepforge, IPPP Durham

myFitter - A Python framework for global fits

The myFitter package is a Python module which provides a convenient, object oriented (frequentist) statistical analysis framework for globals fits and hypothesis tests. Its main feature is an improved method for the numerical computation of p-values in non-linear regression models based on importance sampling. Additional features are:

  • computation of maximum likelihood estimates of model parameters,
  • handling of non-linear constraints on the parameter space,
  • computation of profile likelihoods,
  • visualisation of one and two-dimensional confidence regions (via matplotlib),
  • parallelisation.

Citation Guide

The algorithms implemented in myFitter are discussed in

M. Wiebusch, "Numerical Computation of p-values with myFitter," Comput. Phys. Commun. (2013), DOI: 10.1016/j.cpc.2013.06.008, [arXiv:1207.1446v2]

If you use myFitter for a scientific publication, please cite this article.

Installation and requirements

myFitter requires Python 2.7 and the following packages:

  • NumPy (version 1.8 or later),
  • SciPy (version 0.13 or later),
  • vegas (version 2.2.1 or later),
  • cloud (version 2.8.5 or later),
  • matplotlib (version 1.3 or later).

myFitter uses the setuptools build system. After downloading the source tarball myFitter-x.y.tar.gz you can install myFitter in your home directory with

tar -zxvf myFitter-x.y.tar.gz
cd myFitter-x.y
python install --user


C++ versions

myFitter was initially written as a C++ class library. You can download the old C++ code below, but please note that this code is no longer maintained.


Please send bugreports to Martin Wiebusch