Despite the long time that has passed since v0.7.0, this is a minor release. Its sole purpose is to provide an easily installable form of ODL as it was in 0.7, but updated to be compatible with modern versions of the libraries we depend on (in particular NumPy and SciPy, which have made several breaking changes since).
The reason this version is still numbered 0.x is that ODL itself will soon have to undergo changes that may break existing user code. The upcoming version 1.0 is intended to move away from the current status where essentially all computational objects are wrappers around NumPy arrays, and instead be more agnostic with respect to the backend. This will both allow faster execution of existing inverse-modelling setups by keeping data on e.g. a GPU, as well as tighter integration with deep learning frameworks. The implementation of this for PyTorch is work in progress. It does however require forgoing some of the convenience utilities that made ODL space-elements almost perfect drop-in replacements for NumPy arrays.
This is the final release which prioritises allowing NumPy-designed algorithms to be used on ODL objects.