Pca

Developing new functionalities is really cool. However, when making incremental improvements over time, the code complexity also gradually increases. I took the time to refactor the entire plotting part. When using this version, you likely need to rename some input parameters in your code. But it is worth it because the plots became even more beautiful!

* scattering is now performed in scatterd library
* Many input parameters for plotting are aligned to the scatter functionality of matplotlib.
* for plotting, some parameters such as textlabel are removed because were redundant.
* for plotting, the parameter y is renamed into labels
* it is now possible to add density and gradient into the plots and keeping the plot look nice
* Changing the ordering of the density layer is possible (on top or below)
* Fix for 3d plot and the positioning of the text labels
* High improvements in plotting speed when having many data points!
* updated documentation, docstrings and readme

* Removed wget as a dependency
* Added functionality to specify markers per sample
* Improved functionality to customize alpha transparency per sample
* Improved functionality to customize size per sample
* Updated docstrings

* fix for multiple testing
* Added parameter `ax` to plots

* set default std=3 wich is more common for outlier detection
* Multiple test corrections for the hotelling t2 test
* `multipletests `is set in the predict function and not during initialization anymore.
* `y_proba `is the corrected Pvalue. `Praw `is the uncorrected Pvalue in the output dataframe

* Font color inherits the arrow color (default).
* Font colors can be adjusted in the plots.
* Sizes of the scatter can be adjusted with parameter `s`.
* Colors of the scatter ben be adjusted with parameter `c`.
* `fig` can be given as an input parameter to make iterative changes to the plot.

Examples can be found [here](https://erdogant.github.io/pca/pages/html/Examples.html#colors-in-plots).

* `sklearn` changed into `scikit-learn` in both requirements and setup file.