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A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
A side-by-side editorial comparison of forrel and GeneNMF — release velocity, themes, recent moves, and the top alternatives to consider.
forrel is getting faster at the simulations forensic kinship work actually spends its time on.
forrel handles forensic pedigree analysis: kinship likelihood ratios, profile simulation, relationship checking, and missing person calculations. Version 1.9.0 synced with pedtools 2.11.0's loop handling, which the release notes credit with enabling complex pedigrees that were previously intractable, and moved profileSim() to mirai for parallelism. It also added fEstimate() for inbreeding coefficients and parentChildLikelihood() as a fast path for the simplest case.
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.
forrel handles forensic pedigree analysis: kinship likelihood ratios, profile simulation, relationship checking, and missing person calculations. Version 1.9.0 synced with pedtools 2.11.0's loop handling, which the release notes credit with enabling complex pedigrees that were previously intractable, and moved profileSim() to mirai for parallelism. It also added fEstimate() for inbreeding coefficients and parentChildLikelihood() as a fast path for the simplest case.
Two long threads run through the window. One is making the common operations cheap: faster simulations through reorganized likelihood calculations, a dedicated parent-child path, dropped map attribute preservation, log-likelihoods to avoid underflow in kinshipLR(). The other is making relationship checking presentable, with checkPairwise() growing ggplot2 and plotly output, verbal relationship descriptions, and bootstrap p-values. Reference data is maintained alongside both, with the FORCE SNP panel completed and an X-chromosomal counterpart added.
With profileSim() on mirai and the loop handling synced, the next likely step is extending mirai parallelism to the other simulation-heavy functions such as exclusionPower() and the bootstrap in checkPairwise().
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 forrel or GeneNMF.
A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
A mature recurrent-event toolkit in careful maintenance, shedding weight rather than adding surface.
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.
See all forrel alternatives → · See all GeneNMF alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. forrel 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. forrel 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 forrel alternatives in Analytics are ranked by recent ship velocity. Browse the "forrel alternatives" section above for the current picks, or visit /alternatives/forrel 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.