pins
pins keeps adding a storage backend per release while retiring its original API
Comparison · Analytics
dials vs embed
A side-by-side editorial comparison of dials and embed — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodels
dials vs embed: at a glance
What is dials?
dials is quietly registering the tuning parameters for tidymodels' deep-learning push
dials defines the parameter objects and grid constructors that tidymodels tunes over, which makes its release notes a reliable early read on what the rest of the stack is about to support. The last two releases are dominated by attention-model parameters — SAINT and tabular deep learning via brulee, TabPFN via parsnip's tab_pfn() — alongside catboost parameters for bonsai and calibration parameters for tailor.
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.
dials vs embed: editorial side-by-side
dials
0.0ANALYTICS
dials is quietly registering the tuning parameters for tidymodels' deep-learning push
◆ Current state
dials defines the parameter objects and grid constructors that tidymodels tunes over, which makes its release notes a reliable early read on what the rest of the stack is about to support. The last two releases are dominated by attention-model parameters — SAINT and tabular deep learning via brulee, TabPFN via parsnip's tab_pfn() — alongside catboost parameters for bonsai and calibration parameters for tailor.
◆ Where it's heading
The grid machinery itself is settled: grid_space_filling() consolidated the older designs, and the grid_*() functions now error rather than warn on the wrong size argument. What keeps moving is the parameter catalog, and it is moving toward neural and foundation-model territory that tidymodels historically left alone. Error-message quality is a steady secondary theme.
◆ Prediction
Expect further parameter objects to land ahead of the parsnip and brulee releases that use them — the attention and tabular-foundation-model work in flight is the clearest thing the entries point to.
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 dials 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 dials or embed.
tsibble
tsibble shipped one release in five and a half years - the data structure is finished
yardstick
yardstick made fairness metrics a first-class part of tidymodels evaluation
tune
tune extends tuning past the model itself to postprocessors, and adds a second parallel backend
leaflet
leaflet relicensed to MIT and finished migrating off R's retired spatial stack
ggpubr
ggpubr reached 1.0.0 with p-value formatting presets for specific journals
Recent activity from dials and embed
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodialsAttention and tabular deep-learning parameters for brulee models
- 4mo agodialsParameters for ordinal_reg() and the tab_pfn() foundation model
- 6mo agoembedstep_umap() zero-component bug fixed
- 8mo agoembedCompatibility with all xgboost versions
- 11mo agoembedstep_lencode() adds analytical likelihood encoding with pooling
- 11mo agodialsprop_terms() for supervised feature selection recipes
- 1y agodialsCalibration method parameters for classification and regression
- 1y agodialsPostprocessing parameters added; grid size mismatches now error
- 1y agoembedUMAP initial and target_weight become tunable
- 2y agodialsgrid_space_filling() consolidates the space-filling designs
- 2y agoembedkeras and tensorflow moved to Suggests
- 2y agoembedstep_collapse_stringdist() returns factors
Frequently asked questions
What is the difference between dials and embed?
Both compete on the same themes — tidymodels — within Analytics. dials 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 dials better than embed?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dials 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 dials?
Top dials alternatives in Analytics are ranked by recent ship velocity. Browse the "dials alternatives" section above for the current picks, or visit /alternatives/dials 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.