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Comparison · Analytics

probmed vs STACAS

A side-by-side editorial comparison of probmed and STACAS — release velocity, themes, recent moves, and the top alternatives to consider.

probmed vs STACAS: at a glance

FeatureprobmedSTACAS
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themescausal mediation, effect size, semiparametric inference, cross-fittingsingle-cell, batch-correction, data-integration, seurat
Last editorial update8h ago1h ago
WebsiteVisit →Visit →

What is probmed?

probmed went from one probabilistic effect size to a family of them in sixteen days.

probmed computes P_med, a scale-free probabilistic effect size for causal mediation, as part of the Data-Wise mediationverse alongside medfit, medsim and RMediation. Three releases in three weeks took it from a single estimator to four additional families built on a shared cross-fitted corner-EIF core, covering gauge-calibrated, incremental-elasticity and Sobol variance-share versions of the proportion mediated. Distribution is GitHub and r-universe rather than CRAN, with a load-bearing Remotes pin on medfit.

Read the full probmed trajectory →

What is STACAS?

Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.

STACAS integrates single-cell RNA-seq datasets by finding and weighting anchors between them, with rPCA-distance-based downweighting and an optional semi-supervised mode that uses cell type labels to discard inconsistent anchors. IntegrateData.STACAS() performs the integration natively rather than handing off, and StandardizeGeneSymbols() normalises gene naming across datasets before anchors are computed.

Read the full STACAS trajectory →

probmed vs STACAS: editorial side-by-side

P
probmed
ANALYTICS
0.0

probmed went from one probabilistic effect size to a family of them in sixteen days.

◆ Current state

probmed computes P_med, a scale-free probabilistic effect size for causal mediation, as part of the Data-Wise mediationverse alongside medfit, medsim and RMediation. Three releases in three weeks took it from a single estimator to four additional families built on a shared cross-fitted corner-EIF core, covering gauge-calibrated, incremental-elasticity and Sobol variance-share versions of the proportion mediated. Distribution is GitHub and r-universe rather than CRAN, with a load-bearing Remotes pin on medfit.

◆ Where it's heading

The pace is manuscript-driven — estimators arrive with their citations attached and vignettes alongside, and the 0.1.0 notes correct the estimand itself against a manuscript definition rather than fixing a bug in code. Each release adds inference machinery as well as point estimates: percentile-bootstrap intervals and Fieller sets in 0.3.0, a deterministic MBCO interval in 0.2.0 that avoids resampling entirely. The gauge residual and the pmed_sensitivity() helper suggest a growing concern with when the estimand does not decompose at all.

◆ Prediction

0.3.0 shipped a sensitivity helper for shared mediator-outcome confounding and a diagnostic that flags non-decomposability, so the next release most likely extends that diagnostic side rather than adding a fifth estimator family.

S
STACAS
ANALYTICS
0.0

Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.

◆ Current state

STACAS integrates single-cell RNA-seq datasets by finding and weighting anchors between them, with rPCA-distance-based downweighting and an optional semi-supervised mode that uses cell type labels to discard inconsistent anchors. IntegrateData.STACAS() performs the integration natively rather than handing off, and StandardizeGeneSymbols() normalises gene naming across datasets before anchors are computed.

◆ Where it's heading

The method work concentrated in version 2.0 and has been stable since; everything after is Seurat compatibility and operational robustness. Versions 2.1.1 through 2.3.0 track Seurat v5 assays, v3-to-v5 conversion, multi-layer objects and SCT normalisation, with the genuinely useful additions — a reference seed dataset, max.seed.datasets for large-scale integration, min.sample.size — arriving as side effects of that work. The package is from the same lab as GeneNMF, and its release rhythm follows the single-cell ecosystem's upstream churn rather than an internal roadmap.

◆ Prediction

Expect the next release to follow further Seurat object-model changes, which have driven the last three. Nothing in the entries indicates new anchor-scoring or correction methodology in progress.

Alternatives to probmed and STACAS

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either probmed or STACAS.

See all probmed alternatives → · See all STACAS alternatives →

Recent activity from probmed and STACAS

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agoprobmedFour estimator families on a cross-fitted corner-EIF core
  2. 2mo agoprobmedParallel mediators and a resampling-free MBCO interval
  3. 2mo agoprobmedFirst release: pmed() with the estimand corrected
  4. 1y agoSTACASMulti-layer objects and Seurat v3-to-v5 conversion handled
  5. 2y agoSTACASscale.data option for extreme batch effects; gene name conversion table
  6. 3y agoSTACASReference seeding, gene symbol standardisation, large-scale integration path
  7. 4y agoSTACASSemi-supervised integration and rPCA anchor downweighting
  8. 5y agoSTACASSeurat 4.0.0 compatibility and SCTransform support

Frequently asked questions

What is the difference between probmed and STACAS?

They serve adjacent needs but don't currently overlap on shipped themes. probmed and STACAS are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is probmed better than STACAS?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. probmed and STACAS are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to probmed?

Top probmed alternatives in Analytics are ranked by recent ship velocity. Browse the "probmed alternatives" section above for the current picks, or visit /alternatives/probmed for the full list with editorial commentary on each.

What are the best alternatives to STACAS?

Top STACAS alternatives in Analytics are ranked by recent ship velocity. Browse the "STACAS alternatives" section above for the current picks, or visit /alternatives/stacas for the full list with editorial commentary on each.