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bicausality

A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E).

darkeyes.github.io

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edoif

EDOIF is a nonparametric framework based on estimation statistics principle. Its main purpose is to infer orders of empirical distributions from different categories base on a probability of finding a value in one distribution that greater than the expectation of another distribution.

ipadmixture

A data clustering package based on admixture ratios (Q matrix) of population structure analysis.

mdl-multiresolution-regression-framework

mflica

Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction from a set of multivariate time series.

mrreg

The framework for finding multiresolution partitions that have homogeneous linear models from multiresolution dataset.

vltimeseriescausality

A framework to infer causality on a pair of time series of real numbers based on Variable-lag Granger causality and transfer entropy.