Causalnex

* support for newer versions of scikit-learn
* classification report now returns dict in line with scikit-learn

* Plotting now backed by pygraphviz. This allows:
* More powerful layout manager
* Cleaner fully customisable theme
* Out-the-box styling for different node and edge types
* Can now get subgraphs from StructureModel containing a specific node
* Bugfix to resolve issue when fitting CPDs with some missing states in data
* Minor documentation fixes and improvements

Bugfix to resolve broken links in README and minor text issues.

Bugfix to add image to readthedocs

Bugfix to address readthedocs issue.

The initial release of CausalNex.

Thanks for supporting contributions

CausalNex was originally designed by [Paul Beaumont](https://www.linkedin.com/in/pbeaumont/) and
[Ben Horsburgh](https://www.linkedin.com/in/benhorsburgh/) to solve challenges they faced in
inferring causality in their project work.
This work was later turned into a product thanks to the following contributors:
[Philip Pilgerstorfer](https://uk.linkedin.com/in/philippilgerstorfer)
, [Angel Droth](https://www.linkedin.com/in/angeldroth/)
, [Richard Oentaryo](https://www.linkedin.com/in/oentaryo/)
, [Steve Ler](https://www.linkedin.com/in/song-lim-steve-ler-380366106/)
, [Hiep Nguyen](https://vn.linkedin.com/in/hiep-nguyen-87b772105)
, [Gabriel Azevedo Ferreira](https://sg.linkedin.com/in/gabriel-azevedo-ferreira-82415810b)
, [Zain Patel](https://www.linkedin.com/in/zain-patel/)
, [Wesley Leong](https://www.linkedin.com/in/wesleyleong/)
, [Yetunde Dada](https://github.com/yetudada)
, [Viktoriia Oliinyk](https://www.linkedin.com/in/victoria-oleynik/)
, [Roxana Pamfil](https://www.linkedin.com/in/roxana-pamfil-1192053b/)
, [Nisara Sriwattanaworachai](https://www.linkedin.com/in/nisara-sriwattanaworachai-795b357/)
, [Nikolaos Tsaousis](https://www.linkedin.com/in/ntsaousis/)
, [Shuhei Ishida](https://www.linkedin.com/in/shuhei-i/)
, [Francesca Sogaro](https://www.linkedin.com/in/francesca-sogaro/)
, [Deepyaman Datta](https://www.linkedin.com/in/deepyaman/)
, [Ryan Ng](https://www.linkedin.com/in/ryannsj/).

CausalNex would also not be possible without the generous sharing from leading researches in the field of causal inference
and we are grateful to everyone who advised and supported us, filed issues or helped resolve them, asked and answered
questions or simply be part of inspiring discussions.