Roadmap
=========================

correlationMatrix aims to become the most intuitive and versatile tool to analyse discrete correlation data.
This roadmap lays out upcoming steps in this journey.

0.3.X
--------------------------

The 0.3.X family of releases will focus on rounding out a number of functionalities already introduced

- Stressing a set of multi-correlation matrices
- Comparing matrices produced by different estimation methods
- Further documenting the existing functionality
- Further tests, of both code and algorithms

Feature requests, bug reports and any other issues are welcome to log at the `Github Repository <https://github.com/open-risk/correlationMatrix>`_


ToDO List
==================
correlationMatrix is an ongoing project. 0.1 is an alpha release

Several significant extensions are already in the pipeline. You are welcome
to contribute to the development of correlationMatrix by creating Issues or Pull Requests on the github repository


Preprocessing
-------------
- More sophisticated approaches to missing data imputation


Statistical
-----------

- Further validation and characterisation of correlation matrices
- Fixing common problems encountered by empirically estimated correlation matrices
- Confidence intervals
- Additional factor models
- Network models for correlated residuals


Implementation
--------------
- PyPi installation
- Expand Sphinx documentation
- Introduce visualization objects / API
- Testing