v<0.8.4>, <10/13/2020> -- Fix COPOD code inconsistency (issue #239).
v<0.8.4>, <10/24/2020> -- Fix LSCP minor bug (issue #180).
v<0.8.4>, <11/02/2020> -- Add support for Tensorflow 2.
v<0.8.4>, <11/12/2020> -- Merge PR #!02 for categortical data generation.

v<0.8.2>, <07/04/2020> -- Add a set of utility functions.
v<0.8.2>, <08/30/2020> -- Add COPOD and MAD algorithm.
v<0.8.3>, <09/01/2020> -- Make decision score consistent.
v<0.8.3>, <09/19/2020> -- Add model persistence documentation (save and load).

Short summary, we add two new algorithms COPOD and MAD. Moreover, we now provide a short example regrading model save and load.

This is a stable release. Python 2 support will be dropped in the next version.

v<0.8.0>, <05/18/2020> -- Update test frameworks by reflecting sklearn change.
v<0.8.1>, <07/11/2020> -- Bug fix and documentation update

v<0.7.8.1>, <04/07/2020> -- Hot fix for SOD.
v<0.7.8.2>, <04/14/2020> -- Bug Fix for LODA.
v<0.7.9>, <04/20/2020> -- Relax the number of n_neighbors in ABOD and COF.
v<0.7.9>, <05/01/2020> -- Extend Vanilla VAE to Beta VAE by Dr Andrij Vasylenko.
v<0.7.9>, <05/01/2020> -- Add Conda Badge.

Various changes have been made in these two releases:

v<0.7.7>, <12/21/2019> -- Refactor code for combination simplification on combo.
v<0.7.7>, <12/21/2019> -- Extended combination methods by median and majority vote.
v<0.7.7>, <12/22/2019> -- Code optimization and documentation update.
v<0.7.7>, <12/22/2019> -- Enable continuous integration for Python 3.7.
v<0.7.7.1>, <12/29/2019> -- Minor update for SUOD and warning fixes.
v<0.7.8>, <01/05/2019> -- Documentation update.
v<0.7.8>, <01/30/2019> -- Bug fix for kNN (#158).
v<0.7.8>, <03/14/2020> -- Add VAE (implemented by Dr Andrij Vasylenko).
v<0.7.8>, <03/17/2020> -- Add LODA (adapted from tilitools).

The major improvement includes the addition of VAE and LODA, along with multiple minor fixes.

v<0.7.6>, <12/18/2019> -- Update Isolation Forest and LOF to be consistent with sklearn 0.22.
v<0.7.6>, <12/18/2019> -- Add Deviation-based Outlier Detection (LMDD).

The major update is about the compatibility fix for the newly released sklearn 0.22, and LMDD module built by @John-Almardeny

This minor update includes the following items (most of them are bug fix and documentation improvement):

v<0.7.5>, <09/24/2019> -- Fix one dimensional data error in LSCP.
v<0.7.5>, <10/13/2019> -- Document kNN and Isolation Forest's incoming changes.
v<0.7.5>, <10/13/2019> -- SOD optimization (created by John-Almardeny in June).
v<0.7.5>, <10/13/2019> -- Documentation updates.

Multiple bug fixes are introduced:

• Fix issue in CBLOF for n_cluster discrepancy.
• Fix issue #23 that kNN fails with Mahalanobis distance.
• Fix for sklearn new behaviour FutureWarning.

Improved documentation:

• Update docs with media coverage.
• Major documentation update for JMLR.
• Add License info and show support to 996.ICU!
• Redesign ReadMe for clarity.

Deprecate two key APIs: fit_predict and fit_predict_score.

Add some new utility functions, e.g., generate_data_clusters.

This release further improves package stability and comprehensiveness.

A set of new models are added:

• LSCP: Locally Selective Combination of Parallel Outlier Ensembles
• XGBOD: Extreme Boosting Based Outlier Detection (Supervised)
• SO_GAAL: Single-Objective Generative Adversarial Active Learning
• MO_GAAL: Multiple-Objective Generative Adversarial Active Learning

Bug fixes are also included, e.g., CBLOF.

Last but not least, a few functions/models are redesigned/optimized:

• Docstring is refactored to numpydoc
• LOCI is optimized with numba
• visualize function is redesigned

Various exciting changes are made in this version.
Welcome Zain Nasrullah and Winston (Zheng) Li to join the core dev team!

New models are added:

• Stochastic Outlier Selection (SOS)
• Local Correlation Integral (LOCI)

New continuous integration tools are enabled:

• Appveyor CI
• CodeClimate
• CircleCI

Some bugs are fixed and README is rewritten in rst.