STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
GeneNMF vs nflfastR
A side-by-side editorial comparison of GeneNMF and nflfastR — release velocity, themes, recent moves, and the top alternatives to consider.
GeneNMF vs nflfastR: at a glance
What is GeneNMF?
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.
What is nflfastR?
nflfastR is shedding surface to the rest of nflverse and consolidating on one stats API.
The play-by-play backbone of nflverse, shipping one or two releases a year with long bug-fix lists against decades of NFL data. Since 5.0.0 the package has had a single calculate_stats() entry point that replaces the older calculate_player_stats*() family, backed by an exported nfl_stats_variables table describing every returned column. The last two releases hand work outward — standings moved to nflseedR, and the loaders are now straight re-exports of nflreadr — while fast_scraper_roster(), fast_scraper_schedules() and report() are formally deprecated.
GeneNMF vs nflfastR: editorial side-by-side
GeneNMF
0.0ANALYTICS
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
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.
nflfastR
0.0ANALYTICS
nflfastR is shedding surface to the rest of nflverse and consolidating on one stats API.
◆ Current state
The play-by-play backbone of nflverse, shipping one or two releases a year with long bug-fix lists against decades of NFL data. Since 5.0.0 the package has had a single calculate_stats() entry point that replaces the older calculate_player_stats*() family, backed by an exported nfl_stats_variables table describing every returned column. The last two releases hand work outward — standings moved to nflseedR, and the loaders are now straight re-exports of nflreadr — while fast_scraper_roster(), fast_scraper_schedules() and report() are formally deprecated.
◆ Where it's heading
nflfastR is becoming the parsing and modelling core rather than the whole toolkit. Every recent release either narrows its own API or points users at a sibling package, and the documentation strategy follows: re-exported functions are deliberately undocumented here so nflreadr stays the single source. The remaining in-house work is data correctness — duplicated play IDs, scramble identification, new penalty types — plus keeping the xgboost-backed models running as that dependency moves.
◆ Prediction
The deprecated scrapers and report() are the next things to be removed outright, and the calculate_player_stats*() family should follow, leaving calculate_stats() as the only supported path.
Alternatives to GeneNMF and nflfastR
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 nflfastR.
ggtrace
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
kde1d
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
svines
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
gghighlight
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
rtrek
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all GeneNMF alternatives → · See all nflfastR alternatives →
Recent activity from GeneNMF and nflfastR
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agonflfastRLoaders re-exported from nflreadr; legacy scrapers deprecated
- 11mo agoGeneNMFSingle-sample runs fixed; gene weight definition refined
- 1y agonflfastRStandings handed to nflseedR; R 4.1 now required
- 1y agoGeneNMFMetaprogram composition exposed and custom signature DBs supported
- 1y agonflfastRnflfastR 5.0.0 ⚡
- 1y agoGeneNMFSimilarity heatmap downsampling and meta-program removal
- 2y agoGeneNMFMeta-programs rebuilt on gene weight vectors and cosine similarity ⚡
- 2y agoGeneNMFFirst stable release published to CRAN
- 2y agonflfastRSeason-level conversion rate aggregation fixed
- 2y agonflfastRRaw play-by-play can now be cached and parsed locally
- 3y agonflfastRReverse-dependency tests and dplyr compatibility fixes
Frequently asked questions
What is the difference between GeneNMF and nflfastR?
They serve adjacent needs but don't currently overlap on shipped themes. GeneNMF and nflfastR 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 GeneNMF better than nflfastR?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. GeneNMF and nflfastR 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 GeneNMF?
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.
What are the best alternatives to nflfastR?
Top nflfastR alternatives in Analytics are ranked by recent ship velocity. Browse the "nflfastR alternatives" section above for the current picks, or visit /alternatives/nflfastr for the full list with editorial commentary on each.