Causalnex

* Unlocking cap on following requirements: networkx, pathos, torch and wrapt

* Switch to Pyvis for graph visualisation and remove dependency on Graphviz

* Support newer version of Numpy
* Support newer version of Scikit-learn
* Remove python 3.6, 3.7 support

* Add python 3.9, 3.10 support
* Unlock Scipy restrictions
* Fix bug: infinite loop on lv inference engine
* Fix DAGLayer moving out of gpu during optimization step of Pytorch learning
* Fix CPD comparison of floating point - rounding issue
* Fix set_cpd for parentless nodes that are not MultiIndex
* Add Docker files for development on a dockerized environment

* Add expectation-maximisation (EM) algorithm to learn with latent variables
* Add a new tutorial on adding latent variable as well as identifying its candidate location
* Allow users to provide self-defined CPD, as per 18 and 99
* Generalise the utility function to get Markov blanket and incorporate it within `StructureModel` (cf. 136)
* Add a link to `PyGraphviz` installation guide under the installation prerequisites
* Add GPU support to Pytorch implementation, as requested in 56 and 114 (some issues remain)
* Add an example for structure model exporting into first causalnex tutorial, as per 124 and 129
* Fix infinite loop when querying `InferenceEngine` after a do-intervention that splits
the graph into two or more subgraphs, as per 45 and 100
* Fix decision tree and mdlp discretisations bug when input data is shuffled
* Fix broken URLs in FAQ documentation, as per 113 and 125
* Fix integer index type checking for timeseries data, as per 74 and 86
* Fix bug where inputs to the DAGRegressor/Classifier yielded different predictions between float and int dtypes, as per 140
* Fix bug in set_cpd() where only pd.MultiIndex dataframes were considered which does not account for parentless nodes, as per 146

* Add supervised discretisation strategies using Decision Tree and MDLP algorithms
* Add `BayesianNetworkClassifier` an sklearn compatible class for fitting and predicting probabilities in a BN
* Fixes cyclical import of `causalnex.plots`, as per 106
* Add utility function to extract Markov blanket from a Bayesian Network
* Support receiving a list of inputs for `InferenceEngine` with a multiprocessing option
* Add supervised discretisation strategies using Decision Tree and MDLP algorithms
* Added manifest files to ensure requirements and licenses are packaged
* Fix estimator issues with sklearn ("unofficial python 3.9 support", doesn't work with `discretiser` option)
* Minor bumps in dependency versions, remove prettytable as dependency