  This paper introduces the first, open source software library for Constraint
Consistent Learning (CCL). It implements a family of data-driven methods that
are capable of (i) learning state-independent and -dependent constraints, (ii)
decomposing the behaviour of redundant systems into task- and null-space parts,
and (iii) uncovering the underlying null space control policy. It is a tool to
analyse and decompose many everyday tasks, such as wiping, reaching and
drawing. The library also includes several tutorials that demonstrate its use
with both simulated and real world data in a systematic way. This paper
documents the implementation of the library, tutorials and associated helper
methods. The software is made freely available to the community, to enable code
reuse and allow users to gain in-depth experience in statistical learning in
this area.
