facebookresearch/balance 0.18.0 on GitHub

New Features

Implemented r_indicator() with validated sample-variance formula
- Added a public r_indicator(sample_p, target_p) implementation in
  weighted_comparisons_stats using the documented Eq. 2.2.2 formulation
  over concatenated propensity vectors and explicit input-size validation.
- Added validation for non-finite and out-of-range propensity values,
  and expanded unit coverage for formula correctness and edge cases.
- Added BalanceDFWeights.r_indicator() as a convenience wrapper, so
  sample.weights().r_indicator() computes the r-indicator directly.

Sample.design_effect() is deprecated — use sample.weights().design_effect() instead.
The method already exists on BalanceDFWeights; the Sample method now emits a
DeprecationWarning and delegates. Will be removed in balance 0.19.0.
Sample.design_effect_prop() is deprecated — use sample.weights().design_effect_prop() instead.
New method added to BalanceDFWeights. Will be removed in balance 0.19.0.
Sample.plot_weight_density() is deprecated — use sample.weights().plot() instead.
Will be removed in balance 0.19.0.
Sample.covar_means() is deprecated — use sample.covars().mean() instead
(with .rename(index={'self': 'adjusted'}).reindex([...]).T for the same format).
Will be removed in balance 0.19.0.
Sample.outcome_sd_prop() is deprecated — use sample.outcomes().outcome_sd_prop() instead.
New method added to BalanceDFOutcomes. Will be removed in balance 0.19.0.
Sample.outcome_variance_ratio() is deprecated — use sample.outcomes().outcome_variance_ratio() instead.
New method added to BalanceDFOutcomes. Will be removed in balance 0.19.0.

Added CLAUDE.md project context files for Claude Code users, covering architecture,
build/test instructions (Meta and open-source), code conventions, and pre-submit checklist.
Updated .github/copilot-instructions.md review checklist to reduce duplication with
CLAUDE.md and add missing conventions (MIT license header, from __future__ import annotations,
factory pattern, seed fixing, deprecation style).

prepare_marginal_dist_for_raking / _realize_dicts_of_proportions: fixed memory explosion from LCM expansion
- When proportions had high decimal precision or many covariates were passed,
  the LCM of the individual per-variable array lengths could reach tens of
  millions (or more), causing OOM crashes.
- Both functions now accept a max_length parameter (default 10000). When
  the natural LCM exceeds max_length, the output is capped at max_length
  rows and counts are allocated via the Hare-Niemeyer (largest remainder)
  method, which guarantees the total stays exactly max_length with minimal
  rounding error per category.
- A warning is logged whenever the cap is applied.
- A new internal helper _hare_niemeyer_allocation implements the allocation logic.

Full Changelog: 0.17.0...0.18.0