Pyntbci

Added
- Added `min_time` to stopping methods in `stopping`
- Added `max_time` to `CriterionStopping` in `stopping`

Changed

Fixed
- Fixed fit exception in `DistributionStopping` in `stopping`

Added
- Added `tmin` to `encoding_matrix` in `utilities`
- Added `tmin` to `rCCA` in `classifiers`

Changed

Fixed

Added
- Added `find_neighbours` and `find_worst_neighbour` to `utilities`
- Added `optimize_subset_clustering` to `stimulus`
- Added `optimize_layout_incremental` to `stimulus`
- Added `stimplot` to `plotting`

Changed
- Changed order of tutorials and examples

Fixed
- Fixed `max_time` in all `stopping` classes to deal with "partial" segments

Added
- Added `ValueStopping` to `stopping`
- Added parameter `distribution` to `DistributionStopping` in `stopping`

Changed
- Changed `envelope_rms` to `rms` in `envelope`
- Changed `envelope_gammatone` to `gammatone` in `envelope`
- Changed `BetaStopping` in `stopping` to `DistributionStopping`

Fixed
- Fixed default `CCA` in `transformers` to `inv`, not `pinv`
- Fixed `seed` for `make_m_sequence` and `make_gold_codes` in `stimulus` to not be full zeros

Added

Changed

Fixed
- Fixed default `CCA` in `transformers` to `inv`, not `pinv`

Added
- Added `pinv` to `utilities`
- Added `alpha_x` to `CCA` in `tranformers`
- Added `alpha_y` to `CCA` in `tranformers`
- Added `alpha_x` to `eCCA` in `classifiers`
- Added `alpha_t` to `eCCA` in `classifiers`
- Added `alpha_x` to `rCCA` in `classifiers`
- Added `alpha_m` to `rCCA` in `classifiers`
- Added `squeeze_components` to `rCCA`, `eCCA`, `eTRCA` in `classifiers'

Changed
- Changed `numpy` typing of `np.ndarray` to `NDArray`
- Changed `cca_` and `trca_` attributes to be `list` always in `eCCA`, `rCCA` and `eTRCA`
- Changed `scipy.linalg.inv` to `pyntbci.utilities.pinv` in `CCA` of `transformers`
- Changed `decision_function` and `predict` of `classifiers` to return without additional dimension for components if `n_components=1` and `squeeze_components=True`, both of which are defaults

Fixed