rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
modelbased vs modeldata
A side-by-side editorial comparison of modelbased and modeldata — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
modelbased vs modeldata: at a glance
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.
What is modeldata?
The tidymodels example-data package grows one dataset at a time, on nobody's schedule
modeldata exists to supply the datasets and simulation functions that tidymodels documentation, tests, and teaching material depend on. Releases arrive roughly once or twice a year and consist almost entirely of new data sets plus occasional simulation methods. The most recent work adds a Worley (1987) regression simulation and moves the package off the magrittr pipe onto base R's.
modelbased vs modeldata: editorial side-by-side
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.
modeldata
0.0ANALYTICS
The tidymodels example-data package grows one dataset at a time, on nobody's schedule
◆ Current state
modeldata exists to supply the datasets and simulation functions that tidymodels documentation, tests, and teaching material depend on. Releases arrive roughly once or twice a year and consist almost entirely of new data sets plus occasional simulation methods. The most recent work adds a Worley (1987) regression simulation and moves the package off the magrittr pipe onto base R's.
◆ Where it's heading
Two lines run through the history: broadening coverage of task types — ordinal classification, multinomial, regression, QSAR-style chemistry data — and building out synthetic simulation so tutorials can demonstrate a method without shipping a real dataset for it. The simulation side has grown from a single regression generator into a family with logistic and multinomial variants and a keep_truth option that exposes the error-free outcome. Infrastructure changes appear only when the wider tidyverse moves, as the base-pipe transition shows.
◆ Prediction
The pattern points to another simulation method or a dataset filling a task type the collection still lacks, rather than any change in what the package does.
Alternatives to modelbased and modeldata
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 modelbased or modeldata.
writexl
writexl spent nine years refusing to do formatting, then shipped all of it in 2.0.0.
rgbif
rgbif is steadily pushing users off paged searching and onto real downloads.
rstanarm
rstanarm is community-maintained now, tracking Stan and lme4 rather than adding models.
taxa
taxa started a ground-up rewrite in 2021 and has published almost nothing since.
rotl
rotl's whole release history is keeping name matching honest against a moving taxonomy.
See all modelbased alternatives → · See all modeldata alternatives →
Recent activity from modelbased and modeldata
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
- 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()
- 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
- 11mo agomodeldatamodeldata 1.5.1 fixes documentation and column-name typos
- 1y agomodeldatamodeldata 1.5.0 adds a Worley (1987) regression simulation
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
- 2y agomodeldatamodeldata 1.4.0 adds the cat_adoption data set
- 2y agomodeldatamodeldata 1.3.0 adds the deliveries data set
- 3y agomodeldatamodeldata 1.2.0 adds eight data sets across regression and classification
- 3y agomodeldatamodeldata 1.1.0 adds logistic and multinomial simulation plus keep_truth
Frequently asked questions
What is the difference between modelbased and modeldata?
Both compete on the same themes — r-package — within Analytics. modelbased and modeldata 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 modelbased better than modeldata?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. modelbased and modeldata 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 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.
What are the best alternatives to modeldata?
Top modeldata alternatives in Analytics are ranked by recent ship velocity. Browse the "modeldata alternatives" section above for the current picks, or visit /alternatives/modeldata for the full list with editorial commentary on each.