TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of GeneNMF and nswgeo — release velocity, themes, recent moves, and the top alternatives to consider.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
GeneNMF applies non-negative matrix factorization to single-cell expression data to find gene programs, then consolidates programs recurring across samples into meta-programs. Version 0.6.0 replaced the consolidation method: instead of reducing each program to a gene set and taking a consensus, it retains full gene weight vectors and compares them by cosine similarity. Later releases have built reporting and control around that core — a metaprogram composition matrix showing which samples contributed, custom signature databases for enrichment testing, and the ability to drop meta-programs from results.
NSW boundary data for R, refreshed as the official sources move
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
GeneNMF applies non-negative matrix factorization to single-cell expression data to find gene programs, then consolidates programs recurring across samples into meta-programs. Version 0.6.0 replaced the consolidation method: instead of reducing each program to a gene set and taking a consensus, it retains full gene weight vectors and compares them by cosine similarity. Later releases have built reporting and control around that core — a metaprogram composition matrix showing which samples contributed, custom signature databases for enrichment testing, and the ability to drop meta-programs from results.
The package is moving from producing meta-programs to letting users interrogate and constrain how they were formed. Composition matrices, the drop function and downsampled similarity heatmaps all serve inspection rather than derivation. The parameters added alongside the 0.6.0 rewrite — specificity weighting, cumulative weight thresholds, confidence defined as the fraction of programs containing a gene — turn what were fixed internal choices into stated, tunable ones.
Recent releases have been fixes and compatibility work rather than method changes, so the core approach appears settled. The dependency on an RcppML version not on CRAN is the loose end most likely to force the next release.
nswgeo packages New South Wales geographic boundaries for R — suburbs, postcodes, local government areas, Primary Health Networks and Local Health Districts — as ready-to-plot sf datasets. The 0.6.0 release refreshes nearly all of them against new upstream sources, moving postcodes to 2021 ABS boundaries and taking LHD boundaries from a new official feed. It is maintained by cidm-ph alongside the mapping packages that consume it, including ggmapinset.
Every release is dictated by an upstream release calendar rather than a roadmap: the 2023 ASGS, then 2024, then the 2021 ABS postcode boundaries and the new LHD source. That makes field-name churn the package's defining hazard — LGA_NAME_2021 to LGA_NAME_2023 to LGA_NAME_2024, and now lhd_name carrying a Local Health District suffix. The maintainer's habit of registering compatibility aliases through cartographer shows an awareness that these renames break downstream code silently.
Expect the next release to track the following ASGS edition with another round of field renames, and any new content to stay in the health-geography area the package's users work in.
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 GeneNMF or nswgeo.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
The area-proportional Euler diagram package is finished software, and maintained like it.
See all GeneNMF alternatives → · See all nswgeo alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. GeneNMF and nswgeo 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. GeneNMF and nswgeo 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 GeneNMF alternatives in Analytics are ranked by recent ship velocity. Browse the "GeneNMF alternatives" section above for the current picks, or visit /alternatives/genenmf for the full list with editorial commentary on each.
Top nswgeo alternatives in Analytics are ranked by recent ship velocity. Browse the "nswgeo alternatives" section above for the current picks, or visit /alternatives/nswgeo for the full list with editorial commentary on each.