nat.templatebrains
The natverse package that taught neuron data to remember which brain space it lives in.
Comparison · Analytics
invasimapr vs sdsfun
A side-by-side editorial comparison of invasimapr and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
invasimapr vs sdsfun: at a glance
What is invasimapr?
invasimapr halved its install size and became citable; the science stayed put.
invasimapr estimates species invasiveness and site invasibility from trait, environmental and resident-community data, exposing a traits → competition → invasion-fitness pipeline behind seven high-level wrappers. Its three releases are all packaging and standards work: a first citable archive in June 2026, then a maturity release bringing it in line with the B-Cubed software development guide. The one behavioral addition in that release is an opt-in standardise_inputs argument on compute_invasion_fitness(), off by default.
What is sdsfun?
A spatial-statistics utility package exists to be depended on, and is built accordingly.
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
invasimapr vs sdsfun: editorial side-by-side
invasimapr
0.0ANALYTICS
invasimapr halved its install size and became citable; the science stayed put.
◆ Current state
invasimapr estimates species invasiveness and site invasibility from trait, environmental and resident-community data, exposing a traits → competition → invasion-fitness pipeline behind seven high-level wrappers. Its three releases are all packaging and standards work: a first citable archive in June 2026, then a maturity release bringing it in line with the B-Cubed software development guide. The one behavioral addition in that release is an opt-in standardise_inputs argument on compute_invasion_fitness(), off by default.
◆ Where it's heading
The pressure is toward being installable and auditable rather than more capable — install slimmed from roughly 100 MB to 56 MB, R CMD check warnings and notes resolved, sp moved to Suggests, a Darwin Core-aligned data dictionary added, and a Zenodo concept DOI with CITATION.cff, codemeta.json and .zenodo.json. The package moves in lockstep with its B-Cubed sibling dissmapr, tagged within minutes of each other at both 0.1.0 and 0.2.1, which points at project-level standards deadlines rather than independent release decisions. Trait dispersion metrics and scenario exploration remain on the roadmap.
◆ Prediction
Standards compliance is now complete and the roadmap names functional trait dispersion metrics and scenario exploration tools, so the next release is the first that can plausibly be about invasion ecology rather than packaging.
sdsfun
0.0ANALYTICS
A spatial-statistics utility package exists to be depended on, and is built accordingly.
◆ Current state
sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.
◆ Where it's heading
This is infrastructure for a family of packages rather than an end-user tool, and the changelog says so directly — functions were added to support gdverse and sesp, and moran_test was migrated in from geocomplexity. That migration pattern is the defining move: capability consolidates here so the downstream packages can share it instead of each carrying its own copy. Growth has slowed as that consolidation completed, leaving correctness and dependency upkeep.
◆ Prediction
Given the package moves when its dependents need something, the next release most likely brings in another shared function or responds to a downstream requirement rather than following its own plan. Armadillo and CRAN check changes remain the reliable source of maintenance work.
Alternatives to invasimapr and sdsfun
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 invasimapr or sdsfun.
nat.nblast
The NBLAST neuron-similarity engine is stable code on life support, shipping once every few years.
RandomWalker
A random-walk generator that outgrew one dimension and renamed its core column to prove it.
tbrf
Time-based rolling statistics for water-quality data, finally getting plotting and padding built in.
roclang
A roxygen2 documentation-reuse helper whose release notes are mostly upstream damage control.
gratia
The tidy front-end for GAMs, now stable enough that upstream ggplot2 sets its release calendar.
See all invasimapr alternatives → · See all sdsfun alternatives →
Recent activity from invasimapr and sdsfun
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoinvasimaprVersion bump; notes identical to 0.2.0
- 1mo agoinvasimaprB-Cubed standards alignment; install slimmed to 56 MB
- 1mo agoinvasimaprinvasimapr v0.1.0: First citable release
- 10mo agosdsfunPackage load stops touching the RNG state
- 1y agosdsfunUnified partial correlation testing and head/tails discretization
- 1y agosdsfunMissing-value handling added to linear trend removal
- 1y agosdsfunCovariate-based detrending and long-to-matrix spatial reshaping
- 1y agosdsfunSpatially constrained hierarchical clustering and SPADE estimation
- 1y agosdsfunFast geodetector q-value estimator added
Frequently asked questions
What is the difference between invasimapr and sdsfun?
They serve adjacent needs but don't currently overlap on shipped themes. invasimapr and sdsfun 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 invasimapr better than sdsfun?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. invasimapr and sdsfun 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 invasimapr?
Top invasimapr alternatives in Analytics are ranked by recent ship velocity. Browse the "invasimapr alternatives" section above for the current picks, or visit /alternatives/invasimapr for the full list with editorial commentary on each.
What are the best alternatives to sdsfun?
Top sdsfun alternatives in Analytics are ranked by recent ship velocity. Browse the "sdsfun alternatives" section above for the current picks, or visit /alternatives/sdsfun for the full list with editorial commentary on each.