ggtrace
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
Comparison · Analytics
healthyR.ts vs STACAS
A side-by-side editorial comparison of healthyR.ts and STACAS — release velocity, themes, recent moves, and the top alternatives to consider.
healthyR.ts vs STACAS: at a glance
What is healthyR.ts?
healthyR.ts keeps adding time-series helpers, then quietly breaks the old ones to modernise them.
A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.
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.
healthyR.ts vs STACAS: editorial side-by-side
healthyR.ts
0.0ANALYTICS
healthyR.ts keeps adding time-series helpers, then quietly breaks the old ones to modernise them.
◆ Current state
A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.
◆ Where it's heading
Two threads, both consistent. The functional one is coverage of the stationarity workflow — transform, test, auto-stationarize, plot — assembled function by function rather than as a single API. The structural one is convergence on ggplot2 and tidy conventions, retiring xts objects and multi-object return lists as it goes. The package is not afraid to break return shapes to get there, so upgrades are not drop-in.
◆ Prediction
Expect the remaining functions that still return xts objects or bundled lists to get the same ggplot2-only treatment, since ts_ma_plot() was refactored on exactly that rationale.
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.
Alternatives to healthyR.ts 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 healthyR.ts or STACAS.
kde1d
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
svines
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
gghighlight
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
rtrek
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
eiaapi
A thin EIA energy-data client whose whole story is making bulk queries survive the API's limits.
See all healthyR.ts alternatives → · See all STACAS alternatives →
Recent activity from healthyR.ts and STACAS
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agohealthyR.tsRandom walk plot added; ts_ma_plot drops xts for ggplot2 facets
- 1y agoSTACASMulti-layer objects and Seurat v3-to-v5 conversion handled
- 1y agohealthyR.tsInvisible returns dropped; random walk and vva plot fixes
- 2y agoSTACASscale.data option for extreme batch effects; gene name conversion table
- 2y agohealthyR.tsFive log and differencing transform utilities added
- 2y agohealthyR.tsStationarity testing and auto_stationarize added
- 2y agohealthyR.tsSingle example fix
- 3y agoSTACASReference seeding, gene symbol standardisation, large-scale integration path
- 3y agohealthyR.tsBoilerplate fitting uses show_best directly
- 4y agoSTACASSemi-supervised integration and rPCA anchor downweighting ⚡
- 5y agoSTACASSeurat 4.0.0 compatibility and SCTransform support
Frequently asked questions
What is the difference between healthyR.ts and STACAS?
They serve adjacent needs but don't currently overlap on shipped themes. healthyR.ts 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 healthyR.ts better than STACAS?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. healthyR.ts 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 healthyR.ts?
Top healthyR.ts alternatives in Analytics are ranked by recent ship velocity. Browse the "healthyR.ts alternatives" section above for the current picks, or visit /alternatives/healthyr-ts 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.