tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
dials vs probably
A side-by-side editorial comparison of dials and probably — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodels
dials vs probably: 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 probably?
The package that made calibration a step instead of an afterthought.
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
dials vs probably: 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.
probably
0.0ANALYTICS
The package that made calibration a step instead of an afterthought.
◆ Current state
probably started as a small utility for class predictions and equivocal zones, and version 1.0.0 turned it into tidymodels' calibration and uncertainty package: cal_plot_*, cal_estimate_*, cal_validate_* and cal_apply across binary, multiclass and regression problems, plus conformal prediction intervals. Since then the work has been consolidation — a large internal refactor with no API change, split conformal and conformal quantile regression, bound_prediction(), and required_pkgs() and butcher methods so conformal objects can be deployed and stripped.
◆ Where it's heading
The recent releases are about making these objects survive leaving the session. butcher and required_pkgs() methods are what a model needs to be pinned, containerised and served, and their arrival alongside workflows adding a tailor postprocessing stage and vetiver adding probably support points the same way: calibration is being moved out of analysis scripts and into the deployed pipeline. The cal_*_none() reference implementations are the tell that calibration is now something people tune rather than apply once.
◆ Prediction
Expect the calibration functions to be reachable directly from a tuned workflow's postprocessing stage rather than applied to predictions afterwards, following the tailor integration that workflows just shipped.
Alternatives to dials and probably
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 probably.
textrecipes
Text features finally stay sparse all the way to the model.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
patchwork
patchwork stopped being a ggplot composer and became a page composer.
See all dials alternatives → · See all probably alternatives →
Recent activity from dials and probably
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
- 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
- 11mo agodialsprop_terms() for supervised feature selection recipes
- 1y agoprobablyggplot2 test updates and a clearer validation-set error
- 1y agodialsCalibration method parameters for classification and regression
- 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
- 1y agodialsPostprocessing parameters added; grid size mismatches now error
- 2y agodialsgrid_space_filling() consolidates the space-filling designs
- 2y agoprobablyFix grouping sensitivity to variable type
- 3y agoprobablySplit conformal and conformal quantile regression added
- 3y agoprobablyCalibration and conformal inference arrive in tidymodels ⚡
Frequently asked questions
What is the difference between dials and probably?
Both compete on the same themes — tidymodels — within Analytics. dials and probably 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 probably?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. dials and probably 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 probably?
Top probably alternatives in Analytics are ranked by recent ship velocity. Browse the "probably alternatives" section above for the current picks, or visit /alternatives/probably for the full list with editorial commentary on each.