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EOTExplainer rewritten: semicontinuous forward map with analytical scaling $c_\varepsilon = \sqrt{1+\varepsilon}/(1+\varepsilon/2)$ and population backward attribution $W = L \cdot M_w$.
Margin method "auto" is now the default for conf_int(): uses log-scale gap clustering when $d < 30$ (where GMM is unreliable) and mixture (GMM) when $d \geq 30$.
Added margin_method="gap" option: finds the largest multiplicative gap in sorted phi values to separate null from signal features.
conf_int() now accepts verbose=True to print margin determination details (method chosen, gap location, ratio, or GMM parameters).
conf_int() return dict now includes "margin_method" key indicating which method was used.
summary() output now shows the margin method alongside the margin value.
Margin estimation no longer fails on low-dimensional data ($d < 30$): the old GMM-only approach would lump intermediate-valued relevant features into the null component, missing correlated predictive features.