Mamsi

**New Features**
- *k*-fold cross-validation implemented as a method `.kfold_cv()` that can be used for model performance evaluation. This method includes GroupKFold option.
- Monte Carlo cross-validaton (MCCV), also nown as 'random sampling cross-validation' implemented as a method `.montecarlo_cv()` that can be used for model performance evaluation.
- `.estimate_lv()` method now allows to choose between *k*-fold CV and MC-CV using parameter `method`

**Bug Fixes and Behavioural Changes**
- Plot title for `.block_importance()` fixed.
- For regression analysis, MSE metric changed to RMSE
- For `.estimate_lv()` method, parameter `y_continuous=False` was replaced with `classification=True`

**New Features**
- New method 'MamsiPls.block_importance()': Calculate the block importance for each block in the multiblock PLS model and plot the results.

**Minor Bug Fixes and Behaviour Changes**
- Behavioural changes for `MamsiPls.mb_vip()`: The MB-VIP plot is now printed by default, scores are not returned by default. New default arguments (plot=True, get_scores=False).
- Argument changes for `MamsiPls.estimate_lv()`: Old Arguments (no_folds, n_components) changed to (n_slplits, max_components) respectively.
- Plots: 'Verdana' is no longer the default font. The default font changed to Matplotlib default 'DejaVu Sans'.
- Updates to `MamsiStructSearch` class to comply with future warnings - Pandas 3.0.

**Minor Bugs Update**
- Fixes instances where flattened correlation clusters were misaligned to structural clusters.
- Readme licence badge links directly to GitHub licence file (URL).