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
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