  This paper provides a finite sample bound for the error term in the Edgeworth
expansion for a sum of independent, potentially discrete, nonlattice random
vectors, using a uniform-in-$P$ version of the weaker Cram\'{e}r condition in
Angst and Poly (2017). This finite sample bound can be used to derive an
Edgeworth expansion that is uniform over the distributions of the random
vectors. Using this result, we derive a uniform-in-$P$ higher order expansion
of resampling-based distributions.
