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. 184 (2013) 2438, 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 1.0 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 setup.py install --user

Downloads

• myFitter-1.3: downlowad source, browse documentation
• myFitter-1.2: downlowad source, browse documentation
• myFitter-1.1: downlowad source, browse documentation
• myFitter-1.0: downlowad source, browse documentation

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 You can download the old C++ code below, but please note that this code is no longer maintained.

• myfitter-0.2: downlowad source, browse documentation
• myfitter-0.1.1: downlowad source, browse documentation
• myfitter-0.1: downlowad source

Bugs

Please send bugreports to Martin Wiebusch