Introduce result_type() function

* Introduce `result_type()` to find the smallest NumPy dtype that
  accomodates all parameters.
* Support `cupyx.scipy.sparse.csr_matrix` with `dtype=bool`.

README.md

@@ -70,11 +70,23 @@ and to perform efficient conversion to supported formats as needed.
 
 ## Still TODO
 
-* cupyx.sparse formats with dtype `bool`
 * PyTorch arrays
 * SciPy sparse arrays as opposed to SciPy sparse matrices.
 * More detailed cost metric based on more real-world use cases and parameters.
 
+## Changelog
+
+### 0.2.0
+
+* Introduce `result_type()` to find the smallest NumPy dtype that accomodates
+  all parameters. Allowed as parameters are all valid arguments to
+  `numpy.result_type(...)` plus backend specifiers.
+* Support `cupyx.scipy.sparse.csr_matrix` with `dtype=bool`.
+
+### 0.1.1
+
+Initial release
+
 ## Known issues
 
 * `conda install -c conda-forge cupy` on Python 3.7 and Windows 11 may install `cudatoolkit` 10.1 and `cupy` 8.3, which have sporadically produced invalid data structures for `cupyx.sparse.csc_matrix` for unknown reasons. This doesn't happen with current versions. Running the benchmark function `benchmark_conversions()` can help to debug such issues since it performs all pairwise conversions and checks for correctness.