NHSRwaitinglist
Queuing theory packaged for NHS waiting-list managers, one year into a community-built first release.
A side-by-side editorial comparison of condformat and GeneNMF — release velocity, themes, recent moves, and the top alternatives to consider.
A dormant table-formatting package woken by a 20-PR correctness and coverage sweep
condformat applies conditional formatting rules to R data frames and renders them to HTML, LaTeX, Excel and grob output. After 0.10.1 in 2023 it went quiet for nearly three years. Version 0.11.0 breaks that silence with a single-maintainer sweep of roughly twenty pull requests: CI modernized across R-devel, release and oldrel; lockcells fixed for LaTeX and grob output; a rule_fill_bar() crash at zero width; theme_htmlTable() no longer dropping chained arguments; and a .col pronoun added across all rules.
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.
condformat applies conditional formatting rules to R data frames and renders them to HTML, LaTeX, Excel and grob output. After 0.10.1 in 2023 it went quiet for nearly three years. Version 0.11.0 breaks that silence with a single-maintainer sweep of roughly twenty pull requests: CI modernized across R-devel, release and oldrel; lockcells fixed for LaTeX and grob output; a rule_fill_bar() crash at zero width; theme_htmlTable() no longer dropping chained arguments; and a .col pronoun added across all rules.
This is a maintenance-debt payoff rather than a change of direction. The bulk of 0.11.0 is test coverage — render_gtable, knit_print, theme_grob, render_xlsx and rule_fill_bar all gained tests — which reads as a maintainer establishing a safety net before touching anything further. The .col pronoun and scalar recycling in the text rules are the only real API additions, and both smooth inconsistencies rather than extend the rule vocabulary.
With coverage and CI now in place, the next release is more likely to extend rule or output-format support than to continue with fixes, though the three-year gap makes cadence hard to call from these entries alone.
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.
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 condformat or GeneNMF.
Queuing theory packaged for NHS waiting-list managers, one year into a community-built first release.
The plotting companion to simmer, shipping only when the simulator or a graphics dependency moves.
A fast random-graph sampler that spent 0.3.1 fixing what its parameters actually mean.
A young Mathematics Genealogy client spending its first four releases satisfying CRAN.
The natverse package that taught neuron data to remember which brain space it lives in.
The NBLAST neuron-similarity engine is stable code on life support, shipping once every few years.
See all condformat alternatives → · See all GeneNMF alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. condformat and GeneNMF 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. condformat and GeneNMF 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 condformat alternatives in Analytics are ranked by recent ship velocity. Browse the "condformat alternatives" section above for the current picks, or visit /alternatives/condformat for the full list with editorial commentary on each.
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.