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Changelog

From version v4.0 onwards, this package has been renamed to to Associations.jl.

4.2

  • New association measure: AzadkiaChatterjeeCoefficient.

4.1

  • New association measure: ChatterjeeCorrelation.

4.0 (package rename)

The package has been renamed from CausalityTools.jl to Associations.jl.

3.0 (new major release)

This release contains several breaking changes. Any code from before v3.0 will need updating to continue working with v3.0.

The main reason for these breaking changes is that estimators now store the definitions they estimate. This way, we reduce the amount of code we have to write maintain, document and test. At the same time, we hope it is a bit more user-friendly to only relate to "one way of thinking" about estimating association measures.

Breaking changes

Association measures

  • The function association(measure_or_est, input_data...) is the central function that computes all association measures. The first argument is either a measure definition (if it has no estimator), or an estimator. This means that if input_data consists of two input datasets, then a pairwise association is estimated. If input_data consists of three input datasets, then typically a conditional association is estimated (but exceptions are possible).

Independence testing

  • SurrogateTest is now SurrogateAssociationTest
  • SurrogateTestResult is now SurrogateAssociationTestResult

Example systems

  • All example systems are removed.

Crossmap API

The crossmap API has been overhauled.

  • CrossmapEstimators now take the CrossmapMeasure definition as their first argument. For example, you'll have to do ExpandingSegment(CCM(); libsizes = 10:10:50) instead of ExpandingSegment(; libsizes = 10:10:50).

Information API

The information API has been overhauled.

  • Multivariate information measures now store their parameters explicitly, instead of using ComplexityMeasures.EntropyDefinition to do so. For example, to define Shannon-type conditional mutual information, one should do CMIShannon(base = 2) instead of CMIShannon(Shannon(base = 2)).
  • New generic discrete estimator JointDistribution for estimating multivariate information measures. This estimators explicitly computes the joint distribution based on the given discretization, and can be applied to any measure which is defined as a function of a joint distribution.
  • New generic decomposition-based estimator EntropyDecomposition. This estimator computes some multivariate information measure by rewriting the measure definition as a combination of some lower-level measure. For example, CMIShannon can be rewritten as a sum of Shannon entropies. Each of these terms can then be estimated using some differential entropy estimator, e.g. ZhuSingh or Kraskov.
  • New generic decomposition-based estimator MIDecomposition. This estimator computes some multivariate information measure by rewriting the measure definition as a combination of some mutual information measure.
  • New generic decomposition-based estimator CMIDecomposition. This estimator computes some multivariate information measure by rewriting the measure definition as a combination of some conditional mutual information measure.

Bux fixes

  • There was an error in the implementation of PartMutualInformation. It is now fixed using explicit loops for computing the measures from a probability distribution.

2.10

  • Progress bars in some independence tests (surrogate, local permutation) can be enabled by passing keyword show_progress = true in the test constructors.

2.9

Bug fixes

  • Fixed bug in transferentropy function which yielded identical results in both directions for the bivariate case.
  • Fixed bug that occurred when using LocalPermutationTest with TEShannon as the measure and a dedicated TransferEntropyEstimator (e.g. Zhu1 or Lindner). This occurred because the LocalPermutationTest is, strictly speaking, a test using conditional mutual information as the measure. Therefore, naively applying a TransferEntropy measure such as TEShannon would error. This is fixed by performing a similar procedure where the source marginal is shuffled according to local neighborhoods in the conditional marginal. This is similar, but not identical to the CMI-based LocalPermutationTest, and adapts to the specific case of transfer entropy estimation using dedicated transfer entropy estimators instead of some lower-level estimator.
  • Fixed bug in Zhu1 transfer entropy estimator where when box volumes were extremely small, taking the logarithm of volume ratios resulted in Inf values. This was solved by simply ignoring these volumes.

2.8.0

Moved to DynamicalSystemsBase v3.0 (trajectory now returns both the data and the time indices).

Bugfixes

  • Fixed bug in GaussianMI that occurred when the keyword normalize was set to true.

2.7.1

  • Fixed an import warning.

2.7.0

  • New association measure: PMI (part mutual information).

2.6.0

  • New causal graph inference algorithm: PC.

2.5.0

  • New independence test: CorrTest, based on (partial) correlations.

2.4.0

  • Added partial distance correlation measure. To compute it, simply provide a third input argument to distance_correlation.
  • DistanceCorrelation is now compatible with both SurrogateTest and LocalPermutationTest in its conditional form.

2.3.1

  • The MesnerShalisi estimator is now deprecated and renamed to MesnerShalizi (with correct spelling).

2.3.0

  • Significant speed-ups for OCE by sorting on maximal measure, thus avoiding unnecessary significance tests.
  • Default parameters for OCE default lag parameter have changed. Now, τmax = 1, since that is the only case considered in the original paper. We also use the MesnerShalisi CMI estimator for the conditional step, because in contrast to the FPVP estimator, it has been shown to be consistent.
  • Source code for OCE has been drastically simplified by merging the pairwise and conditional parent finding steps.
  • OCE result can now be converted to a SimpleDiGraph from Graphs.jl.

2.2.1

  • infer_graph now accepts the verbose keyword.
  • Fixed a bug in backwards elimination step of OCE algorithm that was caused due to an undefined variable.

2.2

  • Added MCR and RMCD recurrence based association measures, along with the corresponding mcr and rmcd methods.

2.1

  • Bugfix for OCE for certain conditional variable cases.
  • Improve docstring for OCE.
  • Updated readme.
  • Fixed bug related to DifferentialInfoEstimator "unit" conversion.

2.0

The entire package has been completely redesigned. Previous methods are deprecated, and will disappear completely in a v2.X release. For a full overview of new functionality, see the online documentation.

1.4.0

New methods

1.3.0

Example systems