STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of rempsyc and rphylopic — release velocity, themes, recent moves, and the top alternatives to consider.
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.
The R package that puts organism silhouettes on plots keeps widening where they can be drawn.
rphylopic fetches PhyloPic silhouettes and places them into R graphics — base plots, ggplot2 layers, legends, and now phylogenetic trees and igraph networks. The 1.x line has been consistent about two things: adding a new plotting context per release, and steadily replacing its early sizing vocabulary with explicit width and height arguments. Attribution handling is unusually developed for a package this size, with permalinks and per-image credit built into the retrieval functions.
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.
rphylopic fetches PhyloPic silhouettes and places them into R graphics — base plots, ggplot2 layers, legends, and now phylogenetic trees and igraph networks. The 1.x line has been consistent about two things: adding a new plotting context per release, and steadily replacing its early sizing vocabulary with explicit width and height arguments. Attribution handling is unusually developed for a package this size, with permalinks and per-image credit built into the retrieval functions.
Development is expanding the set of places a silhouette can appear rather than changing what the package does. Base plots came first, then ggplot2 aesthetics and legend glyphs, then trees, then network vertices via an igraph shape registered automatically when both packages load. The other running thread is defensive maintenance against upstream churn: retries on failed API calls, fixes for ggplot2 4.0.0, and now an in-memory cache so repeated calls stop hammering the PhyloPic API. The ysize and size deprecation, opened in 1.5.0, is now complete and the arguments are scheduled for removal.
The deprecated ysize and size arguments look set to be removed in the next release, and on the pattern of the last four, another plotting context is a likelier addition than a change to the retrieval layer.
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 rempsyc or rphylopic.
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 rempsyc alternatives → · See all rphylopic alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — ggplot2, r-package — within Analytics. rempsyc and rphylopic 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. rempsyc and rphylopic 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 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.
Top rphylopic alternatives in Analytics are ranked by recent ship velocity. Browse the "rphylopic alternatives" section above for the current picks, or visit /alternatives/rphylopic for the full list with editorial commentary on each.