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intsurv vs STACAS

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

Shared themes:r-package

intsurv vs STACAS: at a glance

FeatureintsurvSTACAS
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themessurvival-analysis, cure-models, censored-data, regularizationsingle-cell, batch-correction, data-integration, seurat
Last editorial update2h ago1h ago
WebsiteVisit →Visit →

What is intsurv?

A Cox cure-rate model package woke up after four years to simplify its own interface.

intsurv fits Cox cure rate models for right-censored survival data where event status may be uncertain — the case where you cannot tell whether a subject experienced the event or was never susceptible to it. The core has been stable since 2019: cox_cure() and its regularized counterpart cox_cure_net(), plus a weighted concordance index and a data simulator. After more than four years without a release, version 0.3.0 arrived in September 2025 and restructured how those two functions are configured rather than adding capability.

Read the full intsurv 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 →

intsurv vs STACAS: editorial side-by-side

I
intsurv
ANALYTICS
0.0

A Cox cure-rate model package woke up after four years to simplify its own interface.

◆ Current state

intsurv fits Cox cure rate models for right-censored survival data where event status may be uncertain — the case where you cannot tell whether a subject experienced the event or was never susceptible to it. The core has been stable since 2019: cox_cure() and its regularized counterpart cox_cure_net(), plus a weighted concordance index and a data simulator. After more than four years without a release, version 0.3.0 arrived in September 2025 and restructured how those two functions are configured rather than adding capability.

◆ Where it's heading

The package has reached the point where the methods are settled and the remaining work is ergonomics. Moving control parameters, M-step settings and penalty specification into cox_cure.control(), cox_cure.mstep() and cox_cure_net.penalty() follows the established R convention of separating tuning from the model formula, and it arrives long after the arguments accumulated. The C++ headers were placed in inst/include as early as 2019 so other packages could link against them, which suggests the implementation was always intended to be reused.

◆ Prediction

The gap between 0.2.2 and 0.3.0 makes cadence a poor basis for prediction. What the entries do support is that the interface rework is unfinished business rather than a prelude to new methods, so consolidation around the new helper functions is the likelier next step.

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 intsurv 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 intsurv or STACAS.

See all intsurv alternatives → · See all STACAS alternatives →

Recent activity from intsurv and STACAS

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

  1. 10mo agointsurvModel configuration moves into dedicated control functions
  2. 1y agoSTACASMulti-layer objects and Seurat v3-to-v5 conversion handled
  3. 2y agoSTACASscale.data option for extreme batch effects; gene name conversion table
  4. 3y agoSTACASReference seeding, gene symbol standardisation, large-scale integration path
  5. 4y agoSTACASSemi-supervised integration and rPCA anchor downweighting
  6. 5y agoSTACASSeurat 4.0.0 compatibility and SCTransform support
  7. 5y agointsurvCross-validated model selection and offset terms added
  8. 6y agointsurvC++ headers relocated so other packages can link them
  9. 7y agointsurvCox cure models for uncertain event status arrive
  10. 7y agointsurvParameter initialization methods added to the alpha
  11. 7y agointsurvAlpha cut for paper submission and simulation reproducibility

Frequently asked questions

What is the difference between intsurv and STACAS?

Both compete on the same themes — r-package — within Analytics. intsurv 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 intsurv better than STACAS?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. intsurv 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 intsurv?

Top intsurv alternatives in Analytics are ranked by recent ship velocity. Browse the "intsurv alternatives" section above for the current picks, or visit /alternatives/intsurv 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.