Adding the VAEAC approach

[LHBO]

In this pull request, we add the vaeac (variational autoencoder with arbitrary conditioning) approach for estimating the conditional Shapley values. The vaeac approach allows for imputing missing values for arbitrary coalitions using a single variational autoencoder. Additionally, the approach supports mixed data. Finally, we have implemented the approach in torch for R.

The vaeac approach was introduced by Olsen et al. (2022), but they only provided a python implementation built on top of the shapr package.

Key points in this pull request are the following:

1. Implemented the vaeac methodology in native torch in R.
2. Incorporated the vaeac approach into the shapr package as an additional method that supports mixed data.
3. Added verbose level to shapr, which allows the user to specify the degree of messages that will be given. This is in addition to progressr's progress bar.
4. Added functionality for sending a pre-trained vaeac model to explain() and for continuing to train the vaeac model for additional epochs if a user is unsatisfied with the accuracy.
5. Added plot functions for monitoring the performance of the vaeac approach.
6. Added plot_SV_several_approaches(), which allows us to plot and compare several Shapley value explanations for the same explicands simultaneously.
7. Added a vignette specifically for the vaeac approach to demonstrate its capabilities and how to use it, in addition to some extra sections/paragraphs in the main vignette.
8. Changed how the vignettes are built to speed up the time. Now, we have to build them manually.
9. Added support for GPU. However, the CPU is often comparable/faster for smaller datasets.

[LHBO]

Fixed all comments

[martinju]

This looks good. Will just run a few test locally, before we merge. Seems that you fixed a typo with another typo. You can just click accept on these and the code will be updated directly on github.