Nltools

Dependency Updates
- Update `scipy.binom_test` -> `scipy.binomtest` in code-base for compatibility with current `scipy` versions

Testing Updates
- Fixed a testing issue on filepath checking on windows
- Forced numpy random seed to 0 for all `pytest` fixtures that use `numpy.random` to generate test data (avoids random test failures due to random data)
- Updated stock GA for setting up conda to v3
- Added m1 macOS runners to grid as experimental (not currently working due to missing hdf5 install on OS)
- Set python 3.11 on macOS runner as experimental due to upstream joblib and 3.11 [issue](https://github.com/joblib/joblib/issues/1544)
- Set docs building to experimental until upstream issues are resolved (docs are outdated with respect to this release!)

Changes
- Drop support for python 3.7 and add support up to 3.11
- Switch from `deepdish` to `h5py` for loading and saving hdf5 `Brain_Data` and `Adjacency` files, with backwards compatible support for hdf5 files created on older versions of nltools

Changes

Fixes
- `nltools.stats.regression` now returns the standard errors of beta estimates
- The canonical output order is: `b`, `se`, `t`, `p`, `df`, `res`
- `Brain_Data` and `Adjacency` will now correctly store the standard error estimates in `stats['sigma']`

Changes

Fixes
- Fixed warnings from `onsets_to_dm` as error-checking wasn't quite right
- Fixed deprecated `nilearn` warnings
- Fixed deprecated `nibabel` `.get_affine()` -> `.affine`

New
- `Brain_Data.similarity` should be dramatically faster and now supports rank correlation: https://github.com/cosanlab/nltools/issues/308 https://github.com/cosanlab/nltools/issues/316 https://github.com/cosanlab/nltools/issues/404
- `Design_Matrix.clean` will raise an error if there are duplicate column name
- Loading `.h5` objects in `Brain_Data` now respects the `mask` argument:

User loads h5 that contains mask so that mask is used instead of the default MNI mask

Brain_Data('brain.h5')

User loads h5 that contains mask but also sets mask argument.
Now mask value takes precedence over whatever mask is in h5
so we issue a warning to the user letting them know on load

Brain_Data('brain.h5', mask='path/to/nifti/mask.nii.gz')

>>> UserWarning(...)

User loads h5 that does NOT contain a mask and doesnt set the mask
argument so the default MNI mask is used, similar to nifti files
This is an implicit fallback just like with niftis

Brain_Data('brain_nomask.h5')

User loads h5 that does NOT contain mask but also sets mask argument
Mask value is used to learn transformation like niftis
No need to warn them about anything

Brain_Data('brain_nomask.h5', mask='path/to/nifti/mask.nii.gz')

General
- No longer pin `pandas` and `deepdish` versions
- Works with the latest version of `sklearn`
- Remove documentation build artifacts from version control

General
- Removed `mne` as a dependency
- Clarify doctstring for `Adjacency.distance_to_similarity` to note we currently only support euclidean and correlation distance

Bug Fixes
- `Path` objects now reliably work for `Brain_Data` and `Adjacency` classes with passing tests
- Fixed major bug in `isps` where hilbert trasform was being applied to the wrong axises

New Features
Adjacency
- new `generate_permutations` method which acts as python _generator_ that can be used for iteration
- new `.cluster_summary` method to summarize with and between cluster distances
- new `.sum` method to add adjacency matrices
- new `.fisher_z_r` method to invert `.fisher_r_z`

Stats
- new `align_states` function that implements the Hungarian Algorithm
- `isps` gains a new `pairwise` argument