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Commits on Mar 29, 2026

• Thread styling kwargs through plotly visualization backends

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• Add AI agent discoverability for practitioner guide

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• Bump version to 2.7.6

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Commits on Mar 28, 2026

• Inline DiD PT step without HonestDiD mention

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• Fix HonestDiD scope in PT step, fix placebo binary time

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• Remove invalid placebo from MultiPeriodDiD handler

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• Fix SDiD handler: compare with CS, not TROP

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• Present control_group as context-dependent, not recommended

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• Add Steps 1-2 reminders to all handlers, fix bibliography

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• Add SA-specific heterogeneity step and fix doc sections

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• Delete dead staggered placebo helper, rewrite Step 6 prose

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• Eliminate shared staggered placebo template entirely

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• Make all handlers fully estimator-specific, add negative assertions

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• Make guidance estimator-specific for PT tests and placebo

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• Add staggered carve-out to llms.txt Step 3 and fix snippets

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• Fix staggered pre-trends guidance and add with/without covariates example

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• Fix remaining step-numbering drift and NaN check for avg_att

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• Align canonical step numbering and fix Bacon/EfficientDiD handlers

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• Address AI review findings on practitioner guardrails

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• Add AI practitioner guardrails based on Baker et al. (2025)

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• Preserve df=0 sentinel on DoseResponseCurve for NaN serialization

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• Reorder solve_logit effective-sample checks: rank reduction before size gate

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• Zero aweight scores for zero-weight rows, fix self.df_ effective count

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• Use positive-weight count for HC1/classical df with zero-weight rows

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• Bacon empty-comparisons guard, document zero-weight df, complete test coverage

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• Complete replicate guard coverage and fix remaining review items

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• Validate replicate scale/rscales, combined_weights contract, fix df metadata

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• Reject replicate designs in TwoWayFixedEffects, fix compute_deff gate

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• Reject replicate designs in unsupported estimators

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• Guard all replicate-df paths for NaN inference when rank <= 1

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• Guard all replicate-df paths for NaN inference when rank <= 1

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• Use pivoted QR for replicate df and NaN inference for rank<=1 designs

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• Use QR-rank with R-compatible tolerance for replicate df_survey

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• Fix replicate df override: only when replicates actually dropped

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• Implement R svrepdesign() scaling: multiplicative scale*rscales, BRR/Fay ignore

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