timetk
timetk swallowed anomalize whole, then went quiet for two years
Comparison · Analytics
datawizard vs probably
A side-by-side editorial comparison of datawizard and probably — release velocity, themes, recent moves, and the top alternatives to consider.
datawizard vs probably: at a glance
What is datawizard?
datawizard is turning easystats' data layer into a general-purpose I/O and reshaping tool
datawizard handles the data preparation half of the easystats stack — reshaping, recoding, describing, and reading and writing files. The 1.x releases have pushed hardest on I/O: parquet via nanoparquet, then password-protected R formats, alongside a run of breaking cleanups in data_to_wide(), data_modify() and describe_distribution().
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.
datawizard vs probably: editorial side-by-side
datawizard
0.0ANALYTICS
datawizard is turning easystats' data layer into a general-purpose I/O and reshaping tool
◆ Current state
datawizard handles the data preparation half of the easystats stack — reshaping, recoding, describing, and reading and writing files. The 1.x releases have pushed hardest on I/O: parquet via nanoparquet, then password-protected R formats, alongside a run of breaking cleanups in data_to_wide(), data_modify() and describe_distribution().
◆ Where it's heading
The package is willing to break its own interfaces to reach behavior users expect from tidyr and friends — data_to_wide() explicitly moved toward pivot_wider() semantics, and data_modify() stopped guessing whether a string was an expression. Output formatting is consolidating behind insight's display() and tinytable. The direction is fewer surprises and more file formats, not more statistics.
◆ Prediction
Expect encryption and format support to extend past R-native files if it continues, and further alignment of print and display behavior with the shared insight infrastructure.
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 datawizard 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 datawizard or probably.
modelbased
modelbased is turning marginal effects into a full contrast grammar
parameters
easystats' parameters package absorbs one more model class every few weeks
tidyposterior
A finished Bayesian model-comparison package in pure maintenance mode
tidypredict
tidypredict now translates the gradient-boosting libraries people actually deploy
tidyclust
tidyclust just tripled the model types it can fit, and handed finalization back to tune
See all datawizard alternatives → · See all probably alternatives →
Recent activity from datawizard and probably
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 3mo agodatawizardEncrypted data files via a password argument on read/write
- 10mo agoprobablyConformal objects gain required_pkgs() and butcher methods
- 10mo agodatawizarddata_to_wide() moves toward pivot_wider() semantics
- 1y agoprobablyggplot2 test updates and a clearer validation-set error
- 1y agodatawizardParquet read and write support via nanoparquet
- 1y agoprobablyCalibration internals refactored; isotonic bootstrap bug fixed
- 1y agodatawizarddata_modify() stops inferring expressions from strings
- 1y agodatawizarddatawizard 1.0.2
- 1y agodatawizarddata_arrange() preserves single-column data frames
- 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 datawizard and probably?
They serve adjacent needs but don't currently overlap on shipped themes. datawizard 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 datawizard better than probably?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. datawizard 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 datawizard?
Top datawizard alternatives in Analytics are ranked by recent ship velocity. Browse the "datawizard alternatives" section above for the current picks, or visit /alternatives/datawizard 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.