  We present the public release of MrMoose, a fitting procedure that is able to
perform multi-wavelength and multi-object spectral energy distribution (SED)
fitting in a Bayesian framework. This procedure is able to handle a large
variety of cases, from an isolated source to blended multi-component sources
from an heterogeneous dataset (i.e. a range of observation sensitivities and
spectral/spatial resolutions). Furthermore, MrMoose handles upper-limits during
the fitting process in a continuous way allowing models to be gradually less
probable as upper limits are approached. The aim is to propose a simple-to-use,
yet highly-versatile fitting tool fro handling increasing source complexity
when combining multi-wavelength datasets with fully customisable filter/model
databases. The complete control of the user is one advantage, which avoids the
traditional problems related to the "black box" effect, where parameter or
model tunings are impossible and can lead to overfitting and/or
over-interpretation of the results. Also, while a basic knowledge of Python and
statistics is required, the code aims to be sufficiently user-friendly for
non-experts. We demonstrate the procedure on three cases: two
artificially-generated datasets and a previous result from the literature. In
particular, the most complex case (inspired by a real source, combining
Herschel, ALMA and VLA data) in the context of extragalactic SED fitting, makes
MrMoose a particularly-attractive SED fitting tool when dealing with partially
blended sources, without the need for data deconvolution.
