Changeforest

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First major release. There have been no changes since the last release.

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**Bug fixes:**

- Fixed bugs when plotting results created with `method="change_in_mean"` or `segmentation="sbs"` or `"wbs"` (Python).

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**Bug fixes**:

- Fixed a bug resulting in no tick-labels being shown on the x-axis when plotting a `BinarySegmentationResult`.

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**New features**:

- New plotting methods `BinarySegmentationResult.plot` and `OptimizerResult.plot` (Python).
- New plotting methods `plot.binary_segmentation_result` and `plot.binary_segmentation_result` (R).
- Expanded documentation (R).
- The `changeforest` function now has default values `method="random_forest"` and `segmentation="bs"` (R).

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**Bug fixes:**

- Fixed a bug in the Python package when passing `random_forest_max_features='sqrt'` to `Control`.

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**Breaking changes:**

- The default value for `model_selection_n_permutations` is now 199.
- The default value for `model_selection_alpha` is now 0.02.
- The default value for `minimal_gain_to_split`, use in the `change_in_mean` setup, is now `log(n) * (d + 1)`, motivated by the BIC and [1].
- The value for `minimal_gain_to_split` no longer gets automatically multiplied by the number of samples `n`.

[1] Yao, Y.-C. (1988). Estimating the number of change-points via Schwarz’ criterion. Statist. Probab. Lett. 6 181–189. MR0919373