Tomotopy

* Since NumPy was introduced in tomotopy, many methods and properties of tomotopy return not just list, but numpy.ndarray now.
* Tomotopy has a new dependency NumPy >= 1.10.0.
* A wrong estimation of tomotopy.HDPModel.infer was fixed.
* A new method about converting HDPModel to LDAModel was added.
* New properties including tomotopy.LDAModel.used_vocabs, tomotopy.LDAModel.used_vocab_freq and tomotopy.LDAModel.used_vocab_df were added into topic models.
* A new g-DMR topic model(tomotopy.GDMRModel) was added.
* An error at initializing tomotopy.label.FoRelevance in macOS was fixed.
* An error that occured when using tomotopy.utils.Corpus created without raw parameters was fixed.

* `tomotopy.Document.path` was added for `tomotopy.HLDAModel`.
* A memory corruption bug in `tomotopy.label.PMIExtractor` was fixed.
* A compile error in gcc 7 was fixed.

* `tomotopy.DTModel` was added into the package.
* A bug in `tomotopy.utils.Corpus.save` was fixed.
* A new method `tomotopy.Document.get_count_vector` was added into Document class.
* Now linux distributions use manylinux2010 and an additional optimization is applied.

* A critical bug related to save and load was fixed. Version 0.6.0 and 0.6.1 have been removed from releases.
* `tomotopy.utils.Corpus` class that manages multiple documents easily was added.
* `tomotopy.LDAModel.set_word_prior` method that controls word-topic priors of topic models was added.
* A new argument `min_df` that filters words based on document frequency was added into every topic model's `__init__`.
* `tomotopy.label`, the submodule about topic labeling was added. Currently, only `tomotopy.label.FoRelevance` is provided.

* A segmentation fault problem was fixed in tomotopy.LLDAModel.add_doc.
* A bug was fixed that infer of tomotopy.HDPModel sometimes crashes the program.
* A crash issue was fixed of tomotopy.LDAModel.infer with ps=tomotopy.ParallelScheme.PARTITION, together=True.

* A bug was fixed that tomotopy.SLDAModel.make_doc doesn't support missing values for y.
* Now tomotopy.SLDAModel fully supports missing values for response variables y. Documents with missing values (NaN) are included in modeling topic, but excluded from regression of response variables.