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v1.3.0 - The Statistics Update - implement GLMs, distributional location-scale, EM mixtures, and conformal risk layer

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@yallioux yallioux released this 06 Sep 18:56
· 24 commits to main since this release
v1.3.0
14468df

[1.3.0] - 2026-09-06

Added

  • Statistics layer (tam.model.statistics) : a modular "how" beside the structural spectrum "what", built on the single P-WLS atom (BaseTAM._solve_pwls_step), so the default loss="l2" path stays bit-identical to ordinary least squares.
    • Reweighting & estimation (estimation/): GLM families (Gamma, Poisson, Binomial), asymmetric expectiles, and robust M-estimators (Huber, Student-t) via StaticTAM(loss=...), driven by an IRLS schedule over the atom.
    • Distributional location-scale fits via a dict formula/loss ({"mu": ..., "sigma": ...}), with automatic Normal/Student-t tail selection, predict_quantiles, cdf, anomaly_score and crps.
    • Mixture of TAM regressions (mixture_components=K) fitted by EM whose M-step is the responsibility-weighted atom.
    • Gaussian copula (GaussianCopulaTAM) binding several distributional margins.
    • Conformal & ACI (statistics.risk): distribution-free CQR intervals, conformal p-values, Mondrian (stratified) calibration (ConformalDistributionalTAM) and streaming Adaptive Conformal Inference, on the static SafetyTAM engine.
    • EVT & epistemic uncertainty: Generalized-Pareto tail scoring (GeneralizedParetoTail, fit_gpd_tail) and Bayesian posterior parameter uncertainty (posterior_prediction).
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