Statistical analysis and visualization of state transition phenomena using transition matrices
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README.md

Intro

transitionMatrix is a Python powered library for the statistical analysis and visualization of state transition phenomena. It can be used to analyze any dataset that captures timestamped transitions in a discrete state space. Use cases include credit rating transitions, system state event logs etc.

You can use transitionMatrix to

  • Estimate transition matrices from historical event data using a variety of estimators
  • Visualize event data and transition matrices
  • Manipulate transition matrices (generators, comparisons etc.)
  • Provide standardized data sets for testing
  • Model transitions using threshold processes

Key Information

NB: transitionMatrix is still in active development. If you encounter issues please raise them in our github repository

Support and Training

Examples

The code documentation includes a large number of examples, jupyter notebooks and more.

Plotting individual transition trajectories

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Sampling transition data

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Estimation of transition matrices using cohort methods

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Estimation of transition matrices using duration methods

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Visualization of a transition matrix

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Generating stochastic process transition thresholds

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Stressing Transition Matrices

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Computation and Visualization of Credit Curves

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