simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of reda and STACAS — release velocity, themes, recent moves, and the top alternatives to consider.
A mature recurrent-event toolkit in careful maintenance, shedding weight rather than adding surface.
reda provides nonparametric mean cumulative function estimation, gamma-frailty rate regression, and event-data simulation for recurrent-event survival analysis. The core API settled at 0.5.0 when Recur() replaced Survr() and the MCF internals moved to C++. Everything since has been consolidation: small argument additions, method completions, and CRAN hygiene.
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.
reda provides nonparametric mean cumulative function estimation, gamma-frailty rate regression, and event-data simulation for recurrent-event survival analysis. The core API settled at 0.5.0 when Recur() replaced Survr() and the MCF internals moved to C++. Everything since has been consolidation: small argument additions, method completions, and CRAN hygiene.
The last three releases contain no new modelling capability at all — a dependency reshuffle, a test-example correction, and a print-order fix. The package is being kept installable and correct rather than extended. Its tightest coupling is to splines2, a sibling package from the same maintainer, which supplies the derivative machinery reda depends on.
Expect continued small-cadence CRAN-compliance releases tracking ggplot2 and splines2 changes. The entries show no in-progress feature work, so a substantive release would have to arrive without warning from this feed.
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.
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.
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.
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 reda or STACAS.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
Conditional density and log-likelihood fill out a vine copula regression package.
A drop-in string API for base R, kept alive by upstream check failures.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. reda 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. reda 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.
Top reda alternatives in Analytics are ranked by recent ship velocity. Browse the "reda alternatives" section above for the current picks, or visit /alternatives/reda for the full list with editorial commentary on each.
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.