rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
mlr3proba vs modelbased
A side-by-side editorial comparison of mlr3proba and modelbased — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
mlr3proba vs modelbased: at a glance
What is mlr3proba?
mlr3proba is shedding weight as its survival work moves into sibling packages
mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.
What is modelbased?
modelbased is turning marginal effects into a full contrast grammar
modelbased computes marginal means, contrasts, and slopes from fitted models, and it ships every one to two months with a consistent shape: new comparison types, broader model support, and steady renaming toward clearer vocabulary. The recent arc runs from marginal effects inequality measures through inequality ratios to an omnibus global test and a post_process argument for multi-step comparisons. Argument names have been settled along the way, with trend becoming slope and an alias left behind.
mlr3proba vs modelbased: editorial side-by-side
mlr3proba
0.0ANALYTICS
mlr3proba is shedding weight as its survival work moves into sibling packages
◆ Current state
mlr3proba provides probabilistic supervised learning for mlr3 — survival analysis, density estimation, and the measures that go with them. Recent releases are almost entirely upkeep: a distr6 fork to work around an upstream problem, an ooplah fix, a predict-type correction, and registration in mlr_reflections$loaded_packages. The one deletion is telling, with LearnerDensPenalized removed after pendensity left CRAN.
◆ Where it's heading
The package is being pared back rather than extended. Its README now points at survdistr and mlr3cmprsk as matured alternatives for parts of what it covers, which reads as scope being handed off, while the Cox proportional-hazards autoplot arrived from mlr3viz in the other direction. Several fixes exist to route around dependencies that broke or disappeared, which is the recurring cost of building on a long chain of specialized CRAN packages.
◆ Prediction
The dependency churn suggests more consolidation — further reliance on survdistr and mlr3cmprsk, and more learners retired when the package underneath them goes unmaintained.
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 mlr3proba 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 mlr3proba or modelbased.
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 mlr3proba alternatives → · See all modelbased alternatives →
Recent activity from mlr3proba and modelbased
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
- 2mo agomlr3probamlr3proba 0.8.10 registers itself in the mlr3 loaded-packages registry
- 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
- 4mo agomlr3probamlr3proba 0.8.9 drops LearnerDensPenalized after pendensity left CRAN
- 5mo agomlr3probamlr3proba 0.8.8
- 5mo agomlr3probamlr3proba 0.8.7
- 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()
- 9mo agomlr3probamlr3proba 0.8.5
- 10mo agomlr3probamlr3proba 0.8.4 takes over the Cox proportional-hazards autoplot
- 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
Frequently asked questions
What is the difference between mlr3proba and modelbased?
Both compete on the same themes — r-package — within Analytics. mlr3proba 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 mlr3proba better than modelbased?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3proba 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 mlr3proba?
Top mlr3proba alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3proba alternatives" section above for the current picks, or visit /alternatives/mlr3proba 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.