STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of rdataone and rempsyc — release velocity, themes, recent moves, and the top alternatives to consider.
The R client for DataONE ships slow, correctness-focused maintenance
rdataone is the R client for the DataONE federated research-data network, handling authentication, upload and retrieval of data packages against member nodes. Recent work is concentrated on correctness in the upload path — rightsHolder persistence, public-read flags applied across all objects in a package, and edge cases in archive() — plus dependency trimming. The feed's version stamps are unreliable: 2.2.2 carries a later publication date than 2.3.0, which cites 2.2.2 as its own predecessor.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.
rdataone is the R client for the DataONE federated research-data network, handling authentication, upload and retrieval of data packages against member nodes. Recent work is concentrated on correctness in the upload path — rightsHolder persistence, public-read flags applied across all objects in a package, and edge cases in archive() — plus dependency trimming. The feed's version stamps are unreliable: 2.2.2 carries a later publication date than 2.3.0, which cites 2.2.2 as its own predecessor.
This is long-cycle infrastructure maintenance, not feature development. Release intervals run to years, and the content is dominated by access-control correctness, CRAN compliance and TLS/platform fixes rather than new client capability. The one consistent thread is hardening how permissions and checksums survive a round trip to a member node.
Expect continued low-frequency releases driven by CRAN check failures and platform TLS changes, with any functional work staying in the upload and permissions path rather than the query surface.
rempsyc produces APA-formatted tables and figures for psychology research — nice_table() for results tables, plus plotting helpers for scatter plots, violin plots, densities and simple slopes. Its releases are CRAN submissions that bundle a long run of development versions, so each entry reads as a digest rather than a single change. The most recent, 0.2.0, added point labelling and per-group correlation statistics to nice_scatter and fixed nice_lm() failing on factor covariates with more than two levels.
Two forces drive this package and neither is its own roadmap. The first is APA style: when the 7th edition advised against beta for standardized coefficients, the package switched its output to italic b with an asterisk. The second is the surrounding ecosystem — formatting is aligned to what lavaanExtra and afex produce, contrast handling was delegated to easystats' modelbased, and Excel correlation matrix export was handed entirely to the correlation package to cut maintenance.
The pattern of delegating functionality to specialist packages while keeping the formatting layer is well established and likely continues. Because releases bundle many small dev versions, the next one will probably again mix plotting refinements with fixes surfaced by upstream changes.
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 rdataone or rempsyc.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all rdataone alternatives → · See all rempsyc alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. rdataone and rempsyc 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. rdataone and rempsyc 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 rdataone alternatives in Analytics are ranked by recent ship velocity. Browse the "rdataone alternatives" section above for the current picks, or visit /alternatives/rdataone for the full list with editorial commentary on each.
Top rempsyc alternatives in Analytics are ranked by recent ship velocity. Browse the "rempsyc alternatives" section above for the current picks, or visit /alternatives/rempsyc for the full list with editorial commentary on each.