leaflet
leaflet relicensed to MIT and finished migrating off R's retired spatial stack
Comparison · Analytics
broom vs embed
A side-by-side editorial comparison of broom and embed — release velocity, themes, recent moves, and the top alternatives to consider.
broom vs embed: at a glance
What is broom?
broom's release calendar is now set by CRAN checks, not by new tidiers.
broom converts model objects from across R's statistical ecosystem into tidy data frames. Four of its last six releases exist purely to resolve R CMD check warnings and errors on r-devel or to absorb upstream package changes. Maintainership passed to Emil Hvitfeldt at 1.0.11.
What is embed?
embed keeps adding encoding steps while shedding its deep-learning dependencies
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
broom vs embed: editorial side-by-side
broom
0.0ANALYTICS
broom's release calendar is now set by CRAN checks, not by new tidiers.
◆ Current state
broom converts model objects from across R's statistical ecosystem into tidy data frames. Four of its last six releases exist purely to resolve R CMD check warnings and errors on r-devel or to absorb upstream package changes. Maintainership passed to Emil Hvitfeldt at 1.0.11.
◆ Where it's heading
Carrying hundreds of tidier methods for model classes it does not own, broom's workload is dominated by other projects' breaking changes and CRAN's evolving checks. New tidier coverage has largely migrated to the packages that define the models, leaving broom as a compatibility surface.
◆ Prediction
Expect the cadence to stay reactive, with releases triggered by r-devel check failures and upstream API shifts rather than expanded model coverage.
embed
0.0ANALYTICS
embed keeps adding encoding steps while shedding its deep-learning dependencies
◆ Current state
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
◆ Where it's heading
Two quiet directions run through these releases. One is making the steps tunable rather than fixed, so they participate properly in tidymodels grids. The other is boundary maintenance: heavy dependencies pushed to Suggests, overlapping steps handed to the package that owns them. Recent releases are thin and fix-driven.
◆ Prediction
Expect further consolidation with textrecipes over which package owns which encoding step, and continued upkeep against xgboost and uwot releases rather than new step families.
Alternatives to broom and embed
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 broom or embed.
ggpubr
ggpubr reached 1.0.0 with p-value formatting presets for specific journals
bigrquery
bigrquery went MIT, then handed its slowest path to the BigQuery Storage API
sparklyr
sparklyr now spends its releases absorbing dbplyr changes and feeding pysparklyr
Seurat
Seurat's centre of gravity has moved from single cells to spatial data and on-disk matrices
insight
insight quietly widens the set of model objects the easystats ecosystem can read
Recent activity from broom and embed
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agobroombroom 1.0.13 relocates a test fixture to clear an r-devel warning
- 3mo agobroombroom 1.0.12 tracks renamed summary() output fields
- 6mo agoembedstep_umap() zero-component bug fixed
- 8mo agobroombroom 1.0.11 transfers maintainership to Emil Hvitfeldt
- 8mo agoembedCompatibility with all xgboost versions
- 11mo agobroombroom 1.0.10 clears an r-devel namespacing warning
- 11mo agoembedstep_lencode() adds analytical likelihood encoding with pooling
- 1y agobroombroom 1.0.9 requires R 4.1 and repairs epiR compatibility
- 1y agobroombroom 1.0.8 fixes cluster-robust intervals, drops orcutt tidiers
- 1y agoembedUMAP initial and target_weight become tunable
- 2y agoembedkeras and tensorflow moved to Suggests
- 2y agoembedstep_collapse_stringdist() returns factors
Frequently asked questions
What is the difference between broom and embed?
They serve adjacent needs but don't currently overlap on shipped themes. broom and embed 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 broom better than embed?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. broom and embed 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 broom?
Top broom alternatives in Analytics are ranked by recent ship velocity. Browse the "broom alternatives" section above for the current picks, or visit /alternatives/broom for the full list with editorial commentary on each.
What are the best alternatives to embed?
Top embed alternatives in Analytics are ranked by recent ship velocity. Browse the "embed alternatives" section above for the current picks, or visit /alternatives/embed for the full list with editorial commentary on each.