  This paper is devoted to presenting in detail the non-perturbative
renormalization group (NPRG) formalism to investigate out-of-equilibrium
systems and critical dynamics in statistical physics. The general NPRG
framework for studying non-equilibrium steady states in stochastic models is
expounded and fundamental technicalities are stressed, mainly regarding the
role of causality and of Ito's discretization. We analyze the consequences of
Ito's prescription in the NPRG framework and eventually provide an adequate
regularization to encode them automatically. Besides, we show how to build a
supersymmetric NPRG formalism with emphasis on time-reversal symmetric
problems, whose supersymmetric structure allows for a particularly simple
implementation of NPRG in which causality issues are transparent. We illustrate
the two approaches on the example of Model A within the derivative expansion
approximation at order two, and check that they yield identical results.
