v0.0.8

[0.0.8] - 2026-07-20

Added

• LFC() and gcate_lfc_batch() results now include log2fc and
  log2fc_se, the base-2 equivalents of the existing natural-log tau and
  std columns. Existing statistics, p-values, and discoveries are unchanged.

Changed

• Older compatible gcate_lfc_batch() caches are upgraded in memory with the
  new log2-scale columns when resumed.
• The Python environment now requires NumPy >2. A separate causarray-r
  environment supplies R 4.4 and a NumPy-2-compatible reticulate, avoiding
  changes to the Python numerical stack for R interoperability.

v0.0.7

[0.0.7] - 2026-07-18

Added

• estimate_propensity_scores() estimates in-sample or out-of-fold treatment
  probabilities without rerunning the outcome model.
• summarize_propensity_scores() and plot_propensity_scores() report and
  visualize common support, tail mass, and inverse-weight effective sample size.
• LFC() now returns both clipped pi_hat and diagnostic pi_hat_raw scores.
• LFC() results include raw AIPW mean_control, mean_treated, and
  estimable diagnostics.
• Propensity and latent-factor association diagnostics in the Perturb-seq
  tutorial.

Changed

• estimate_propensity_scores() now defaults to class-balanced logistic
  weighting, matching LFC() and historical causarray behavior. Pass
  class_weight=None explicitly for calibrated treatment probabilities.

Fixed

• LFC() now always uses standard, unclipped AIPW pseudo-outcomes while
  retaining its established class-balanced propensity nuisance fit to limit
  unrelated result drift. This removes directional bias from clipping negative
  cell-level influence-function values under treatment-group imbalance.
  Calibrated scores remain available with ps_class_weight=None; the legacy
  cell-level clipping option has been removed.
• Valid aggregate counterfactual means are floored only for the final log-ratio
  and delta-method calculation, after averaging the unchanged pseudo-outcomes.
• Nonfinite or nonpositive aggregate counterfactual means are reported as
  non-estimable rather than silently floored before taking a log ratio.
• cross_est=True now enables two-fold nuisance cross-fitting, with an explicit
  K taking precedence.
• Intercept-only propensity models now respect the requested class weighting:
  balanced fits return 0.5 and unweighted fits return the case prevalence.
  Masks are indexed correctly within cross-fitting folds.

v0.0.6

[0.0.6] - 2026-05-31

Added

• Batch-wise fitting API (causarray/gcate.py, causarray/DR_learner.py, causarray/utils.py)

  • fit_gcate_batch(Y, X, A, r, batch_size=10, max_cells=2000, n_ctrl=2000, ...):
    Fits GCATE independently on batches of perturbations. A shared control
    subsample of n_ctrl cells is reused across batches; dispersion is
    pre-estimated once on the control pool. Supports skip_batches to resume
    interrupted runs; reports per-batch wall time and ETA when verbose=True.
  • gcate_lfc_batch(Y, X, A, r, batch_size=10, max_cells=2000, n_ctrl=2000, cache_path=None, ...):
    End-to-end batch pipeline — runs GCATE and LFC per batch, freeing large
    intermediate arrays after each batch. cache_path enables HDF5 disk
    caching via pandas.HDFStore so interrupted runs resume from the last
    completed batch. Returns a concatenated DataFrame with a 'batch' column.
  • LFC_batch(...): deprecated alias for gcate_lfc_batch; emits
    DeprecationWarning and will be removed in a future release.
  • n_batches parameter for fit_gcate_batch and gcate_lfc_batch:
    specifies total number of batches instead of per-batch count; overrides
    batch_size when set.
  • estimate_r(max_cells=N, random_state=0): new parameter that
    automatically subsamples to at most N cells before running JIC
    selection, prioritising control cells.

• Fast GLM backend via crispyx (nb_glm_fast.py, gcate_glm.py)

  • fit_glm_fast(): Batch NB-GLM fitting using crispyx's NBGLMBatchFitter,
    replacing per-gene statsmodels IRLS with vectorized batch IRLS.
  • estimate_disp_fast(): Vectorized method-of-moments dispersion estimation.
  • fit_glm_ondisk(): On-disk streaming GLM fitting for large h5ad files.
  • Per-perturbation fitting (_fit_glm_fast_per_perturbation): for
    multi-treatment data, fits binary (ctrl vs. treatment_k) models
    independently, then assembles the full coefficient matrix.
  • fit_glm_auto(): Routes to fit_glm_fast() when crispyx is available and
    the effective design dimension is small; falls back to statsmodels otherwise.
  • estimate_disp_auto(): Routes to estimate_disp_fast() for large gene
    counts; falls back to statsmodels otherwise.

Fixed

• Numba TBB fork warning: Set NUMBA_THREADING_LAYER_PRIORITY to prefer
  OpenMP over TBB in __init__.py, eliminating fork warnings when Joblib forks
  after Numba parallel execution. Added llvm-openmp to conda dependencies.
• Fast-path threshold (gcate_glm.py): Raised the effective design-dimension
  ceiling so larger r values and wide batch designs correctly use the crispyx path.
• Backend toggle (gcate_glm.py): Re-added _USE_FAST_BACKEND module flag
  and _backend_override() context manager for reliable statsmodels fallback.
• Weighted dispersion (nb_glm_fast.py): Dispersion averaging is now
  cell-count-weighted; low-coverage perturbations contribute proportionally less.
• Control-cell residuals (nb_glm_fast.py): Fixed last-perturbation overwrite
  bug; control-cell deviance residuals and fitted values are now initialised from
  the global covariate model.
• Module-qualified imports (gcate_opt.py, gcate.py, DR_estimation.py):
  Backend toggles now propagate correctly at call time.
• estimate_r bare name (gcate.py): Fixed NameError caused by a bare
  fit_glm_auto reference.
• crispyx availability check (gcate_glm.py): Users without crispyx now get
  a transparent fallback to statsmodels instead of a traceback.

Changed

• ⚠️ alter_min() early-stopping defaults (gcate_opt.py):

  • Default kwargs_es['max_iters'] reduced from 500 → 50.
  • Default tolerance reduced from 1e-3 → 0.0; new scale-invariant
    rel_tol=2e-4 introduced. To reproduce pre-v0.0.6 behavior, pass
    kwargs_es_1=dict(max_iters=500) and kwargs_es_2=dict(max_iters=500).

• ⚠️ BREAKING — LFC() variance and default usevar (DR_learner.py):

  • Default usevar changed from 'pooled' to 'unequal' (Welch). Revert
    with LFC(..., usevar='pooled') if reproducing pre-v0.0.6 results.
  • 'unequal' formula corrected: variance is now s₀²/n₀ + s₁²/n₁
    (standard Welch); the prior version used (s₀²/n₀ + s₁²/n₁)/2
    ("half-Welch"), under-estimating the standard error by √2.
  • p-values now use the t-distribution with Welch-Satterthwaite degrees of
    freedom per gene; the prior version used a Normal approximation.

• alter_min() initialisation, _check_input(), estimate_r(), and
  cross_fitting() now use the auto-dispatch GLM/dispersion paths.

• LFC() now accepts backend: str = "auto" ("fast" forces crispyx,
  "original" forces statsmodels).

• comp_size_factor() vectorized with np.nanmean/np.nanmedian.

Performance

Benchmarked on Perturb-seq data (n = 2,926 cells, p = 3,221 genes, 29 perturbations):

Component	Original	Fast	Speedup
GCATE	331.6 s	298.5 s	1.1×
LFC	87.8 s	65.7 s	1.3×
Total	419.3 s	364.2 s	1.2×

On synthetic data (n = 500, p = 200): 61.5× GLM fit speedup, 7.1× imputation speedup.
Latent factor recovery: mean canonical correlation 0.998. LFC correlation: 0.856.

Additional LFC throughput improvements on the Replogle tutorial dataset
(79,865 cells × 8,563 genes, 200 perturbations, 14 batches):

Change	Speedup contribution	Accuracy impact
Stage 1 max_iter 50 → 5 (NB) / 10 (Poisson)	−10 min	identical (r=1.000)
Stage 1 ≤3,000-cell mixed subsample	−55 min	tau r=0.992, Jaccard=0.80
Stage 2 joint fit	−5 min	tau r=0.9994, Jaccard=0.975
Combined	−70.6 min / 1.48×	tau r=0.9994, Jaccard=0.975

Full-run: 217.5 min → 146.9 min (1.48× faster); sig pairs −0.2%, perts with ≥1 hit −0.6%.

v0.0.5

• Add more tuning options for random-forest classifiers
• Improve the uninformative tests thresholding

Full Changelog: v0.0.4...v0.0.5

v0.0.4

• Add an option for unequal variance inference.
• Add an option for a boolean mask for propensity estimation samples.
• Improve to allow for both 1D and 2D input dimensions.

v0.0.3

• Update estimate_r function.
• Improve verbose options.

v0.0.2

Improve documentation and verbose for GLM functions.

v0.0.1

Update publish-to-pypi.yml