  This paper introduces the KineCluE code that implements the self-consistent
mean-field theory for clusters of finite size. Transport coefficients are
obtained as a sum over cluster contributions (in a cluster expansion
formalism), each being individually computed with KineCluE. This method allows
for the calculation of these coefficients beyond the infinitely dilute limit,
and is an important step in bridging the gap between dilute and concentrated
approaches. Inside a finite volume of space containing the components of a
given cluster, all kinetic trajectories are accounted for in an exact manner.
The code, written in Python, adapts to a wide variety of systems, with various
crystallographic structures (possibly under strain), defects and solute amount
and types, and various jump mechanisms, including collective ones. The code
also features a set of useful tools, such as the sensitivity study routine that
allows for the identification of the most important jump frequencies to get
accurate transport coefficients with minimum computational cost.
