simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
Comparison · Analytics
STACAS vs tbrf
A side-by-side editorial comparison of STACAS and tbrf — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
STACAS vs tbrf: at a glance
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.
What is tbrf?
Time-based rolling statistics for water-quality data, finally getting plotting and padding built in.
tbrf computes rolling statistics over time windows rather than fixed row counts — geometric means, confidence intervals and related summaries indexed by date. That distinction matters for irregularly sampled environmental monitoring data, where a fixed-width window spans different amounts of real time. Version 0.1.7 folds in stat_stepribbon() from ggalt, ships an Entero example dataset for lognormal workflows, and adds na.pad across the tbr_ family.
STACAS vs tbrf: editorial side-by-side
STACAS
0.0ANALYTICS
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.
tbrf
0.0ANALYTICS
Time-based rolling statistics for water-quality data, finally getting plotting and padding built in.
◆ Current state
tbrf computes rolling statistics over time windows rather than fixed row counts — geometric means, confidence intervals and related summaries indexed by date. That distinction matters for irregularly sampled environmental monitoring data, where a fixed-width window spans different amounts of real time. Version 0.1.7 folds in stat_stepribbon() from ggalt, ships an Entero example dataset for lognormal workflows, and adds na.pad across the tbr_ family.
◆ Where it's heading
The package spent its middle releases absorbing upstream breakage — a lubridate duration redefinition, a tibble 3.0.0 subassignment change, tidyselect internals. The 0.1.7 release breaks that pattern: it is the first in five years to add capability rather than repair it, and it does so by internalising a stat from an abandoned dependency instead of relying on it. Cadence remains very low, with a five-year gap between 0.1.5 and 0.1.6.
◆ Prediction
Absorbing stat_stepribbon() directly suggests further vendoring of the plotting layer rather than new statistical functions. The entries do not indicate which rolling statistics, if any, are queued next.
Alternatives to STACAS and tbrf
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 STACAS or tbrf.
lavaanExtra
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
ibis.iSDM
A raster-to-terra migration is the only readable change in a feed of merge notes.
anyflights
A nycflights13 generator whose recent work is all about the data being right.
vinereg
Conditional density and log-likelihood fill out a vine copula regression package.
stringx
A drop-in string API for base R, kept alive by upstream check failures.
Recent activity from STACAS and tbrf
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 0y agotbrfstat_stepribbon vendored in, plus na.pad on all rolling functions
- 1y agoSTACASMulti-layer objects and Seurat v3-to-v5 conversion handled
- 1y agotbrfgm_mean_ci forwards na.rm and zero.propagate correctly
- 2y agoSTACASscale.data option for extreme batch effects; gene name conversion table
- 3y agoSTACASReference seeding, gene symbol standardisation, large-scale integration path
- 4y agoSTACASSemi-supervised integration and rPCA anchor downweighting ⚡
- 5y agoSTACASSeurat 4.0.0 compatibility and SCTransform support
- 6y agotbrfFix internals broken by tibble 3.0.0 subassignment
- 6y agotbrfDate windows recomputed with intervals and periods
Frequently asked questions
What is the difference between STACAS and tbrf?
Both compete on the same themes — r-package — within Analytics. STACAS and tbrf 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 STACAS better than tbrf?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. STACAS and tbrf 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 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.
What are the best alternatives to tbrf?
Top tbrf alternatives in Analytics are ranked by recent ship velocity. Browse the "tbrf alternatives" section above for the current picks, or visit /alternatives/tbrf for the full list with editorial commentary on each.