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Tier A ยท Measurement ยท A2
PHASE 111 ENTRIESTIER A ยท MEASUREMENT

๐Ÿงญ A2 โ€” Conditional dependence

Top pick: GCM / RCoT (GeneralisedCovarianceMeasure (CRAN) ยท RCIT/RCoT (causal-learn)). Fills the gap: Conditional redundancy (CODEC SKIPPED โ€” inert on substrate + no faithful FOSS null) โ€” which the production baseline (Pearson + Spearman + h_norm + Tier-2 MI-vs-target) cannot detect.

Total
11
metrics in this category
Permissive FOSS
2
usable today
GPL
7
license check needed
Unpackaged / no FOSS
2
integration cost
FOSS-READINESS LANES click any metric circle to open its library/repo URL

Catalog entries grouped by adoption-readiness

Lane = how usable the metric is today. Top lane = production-baseline duplicates (Pearson, Spearman). Below = permissive FOSS / GPL / unmaintained / code-released-but-not-packaged / no-FOSS. Per-circle text = first 3 letters of language (Pyt / R / Jav).

PRODUCTION BASELINE
FOSS โ€” permissive
FOSS โ€” GPL
FOSS โ€” unmaintained
Code released, not packaged
No FOSS surfaced
PRODUCTION BASELINE (0 entries) FOSS โ€” permissive (2 entries) Pyt CODEC/FOCI โ€” Python Pyt Graphical Lasso FOSS โ€” GPL (7 entries) Pyt IDTxl R CODEC/FOCI (Azadkia-โ€ฆ R / Generalised Covarianโ€ฆ Pyt RCoT / RCIT (randomiโ€ฆ Pyt CMIknn Pyt PCMCI / PCMCI+ Pyt LPCMCI FOSS โ€” unmaintained (0 entries) Code released, not packaged (0 entries) No FOSS surfaced (2 entries) MAT PMIME Pyt CDCIT (diffusion-basโ€ฆ

Full metric inventory

11 entries ยท sorted by FOSS readiness then name
Metric Library License Lang Maint Complexity Param-free? URL
CMIknntigramite.independence_tests.CMIknnGPLv3Pythonโœ…O(N log N d)yesโ†—
CODEC/FOCI (Azadkia-Chatterjee)FOCIGPLRโœ…O(N log N) per evalnoโ†—
Generalised Covariance Measure (GCM)GeneralisedCovarianceMeasure / pywhy-dodiscoverGPL-3 / BSD-3R / Pythonโœ…O(N) โ€” two regressions + scalar covariance testnoโ†—
IDTxlIDTxlGPLv3Pythonโœ…variesyesโ†—
LPCMCItigramiteGPLv3Pythonโœ…higher than PCMCIyesโ†—
PCMCI / PCMCI+tigramiteGPLv3Pythonโœ…O(N V^2 tau_max k_test)yesโ†—
RCoT / RCIT (randomized conditional)causal-learn (RCIT) / ericstrobl-RCITMIT / GPL-3Python / Rโœ…O(N) โ€” random Fourier features (linear-time KCI approx)yesโ†—
CODEC/FOCI โ€” Pythonxicorpy.select_features_using_fociMITPythonโœ…?โ†—
Graphical Lassosklearn.covariance.GraphicalLassoApache-2Pythonโœ…O(N^3) per iteryesโ†—
CDCIT (diffusion-based CI)CDCITunverifiedPythonโš ๏ธhigh (diffusion training)yesโ†—
PMIMEPMIME-and-TEno LICENSE โ€” commercial use needs author contactMATLAB+Pythonโš ๏ธO(N K L log N)yesโ†—

Cross-references