  Members of the DFT+U family of functionals are increasingly prevalent methods
of addressing errors intrinsic to (semi-) local exchange-correlation
functionals at minimum computational cost, but require their parameters U and J
to be calculated in situ for a given system of interest, simulation scheme, and
runtime parameters. The SCF linear response approach offers ab initio
acquisition of the U and has recently been extended to compute the J
analogously, which measures localized errors related to exchange-like effects.
We introduce a renovated post-processor, the lrUJ utility, together with this
detailed best-practices guide, to enable users of the popular, open-source
Abinit first-principles simulation suite to engage easily with in situ Hubbard
parameters and streamline their incorporation into material simulations of
interest. Features of this utility, which may also interest users and
developers of other DFT codes, include $n$-degree polynomial regression, error
analysis, Python plotting facilities, didactic documentation, and avenues for
further developments. In this technical introduction and guide, we place
particular emphasis on the intricacies and potential pitfalls introduced by the
projector augmented wave (PAW) method, SCF mixing schemes, and non-linear
response, several of which are translatable to DFT+U(+J) implementations in
other packages.
