simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of rtrek and spEDM — release velocity, themes, recent moves, and the top alternatives to consider.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
rtrek bundles Star Trek datasets — book series, timelines, episode transcripts, species and homeworlds, map tile sets — and layers live retrieval on top through memory_alpha() and memory_beta() plus their ma_* and mb_* helpers. Recent releases are almost entirely repairs to that retrieval layer as the source wikis change their page structure.
Spatial causal discovery in R, one exposed method per release
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
rtrek bundles Star Trek datasets — book series, timelines, episode transcripts, species and homeworlds, map tile sets — and layers live retrieval on top through memory_alpha() and memory_beta() plus their ma_* and mb_* helpers. Recent releases are almost entirely repairs to that retrieval layer as the source wikis change their page structure.
The package's centre of gravity shifted once, at 0.2.0, from shipping static data to querying Memory Alpha and Memory Beta at runtime. Everything since has been the maintenance bill for that decision: HTML update fixes, parser improvements, portal retrieval bugs. Note that version numbers on this feed do not track time — 0.2.5 is stamped a year before 0.1.0, and three tags were backfilled within four minutes in November 2020 — so neither rank nor version ordering here indicates release sequence.
Expect the next release to fix retrieval against another Memory Alpha layout change, which is what the last four have done. The entries give no indication of new datasets or functions in progress.
spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.
The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.
Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.
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 rtrek or spEDM.
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. rtrek and spEDM 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. rtrek and spEDM 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 rtrek alternatives in Analytics are ranked by recent ship velocity. Browse the "rtrek alternatives" section above for the current picks, or visit /alternatives/rtrek for the full list with editorial commentary on each.
Top spEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "spEDM alternatives" section above for the current picks, or visit /alternatives/spedm for the full list with editorial commentary on each.