mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
datawizard vs modelbased
A side-by-side editorial comparison of datawizard and modelbased — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:easystats
datawizard vs modelbased: 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 modelbased?
modelbased is turning marginal effects into a full contrast grammar
modelbased computes marginal means, contrasts, and slopes from fitted models, and it ships every one to two months with a consistent shape: new comparison types, broader model support, and steady renaming toward clearer vocabulary. The recent arc runs from marginal effects inequality measures through inequality ratios to an omnibus global test and a post_process argument for multi-step comparisons. Argument names have been settled along the way, with trend becoming slope and an alias left behind.
datawizard vs modelbased: 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.
modelbased
0.0ANALYTICS
modelbased is turning marginal effects into a full contrast grammar
◆ Current state
modelbased computes marginal means, contrasts, and slopes from fitted models, and it ships every one to two months with a consistent shape: new comparison types, broader model support, and steady renaming toward clearer vocabulary. The recent arc runs from marginal effects inequality measures through inequality ratios to an omnibus global test and a post_process argument for multi-step comparisons. Argument names have been settled along the way, with trend becoming slope and an alias left behind.
◆ Where it's heading
The package is building a compositional vocabulary rather than a fixed menu — contrasts of average slopes, contrasts across two numeric predictors, inequality summaries across all outcome categories, and now user-supplied post-processing of comparisons. Support quietly widens underneath, covering nestedLogit, brms finite mixtures, and offsets under population and average estimation. Plotting gets attention in proportion to how often these results are presented rather than tabulated, including collapse_by_group() for showing averaged raw data under mixed-model fits.
◆ Prediction
With post_process and omnibus tests both landed, the likely next step is making these composed comparisons easier to report — formatting or plotting methods for the multi-step results rather than new comparison types.
Alternatives to datawizard and modelbased
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 modelbased.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
timetk
timetk swallowed anomalize whole, then went quiet for two years
parameters
easystats' parameters package absorbs one more model class every few weeks
tidyposterior
A finished Bayesian model-comparison package in pure maintenance mode
See all datawizard alternatives → · See all modelbased alternatives →
Recent activity from datawizard and modelbased
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
- 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
- 3mo agodatawizardEncrypted data files via a password argument on read/write
- 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
- 8mo agomodelbasedmodelbased 0.13.1 adds marginal group-level estimates and as.data.frame()
- 10mo agodatawizarddata_to_wide() moves toward pivot_wider() semantics
- 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
- 1y agodatawizardParquet read and write support via nanoparquet
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
- 1y agodatawizarddata_modify() stops inferring expressions from strings
- 1y agodatawizarddatawizard 1.0.2
- 1y agodatawizarddata_arrange() preserves single-column data frames
Frequently asked questions
What is the difference between datawizard and modelbased?
Both compete on the same themes — easystats — within Analytics. datawizard and modelbased 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 modelbased?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. datawizard and modelbased 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 modelbased?
Top modelbased alternatives in Analytics are ranked by recent ship velocity. Browse the "modelbased alternatives" section above for the current picks, or visit /alternatives/modelbased for the full list with editorial commentary on each.