rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
modelbased vs parameters
A side-by-side editorial comparison of modelbased and parameters — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:easystatsmixed-modelsr-package
modelbased vs parameters: 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 parameters?
easystats' parameters package absorbs one more model class every few weeks
parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.
modelbased vs parameters: 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.
parameters
0.0ANALYTICS
easystats' parameters package absorbs one more model class every few weeks
◆ Current state
parameters extracts and formats coefficients from an enormous range of R model objects, and its releases read as a running ledger of that range expanding — lavaan and lavaan.mi, survey, lcmm, glmmTMB, fixest, marginaleffects, ordinal. Recent versions ship roughly monthly with a mix of new support, new arguments, and fixes for label handling and standard errors. The most consequential recent change is behavioral: post-hoc standardization no longer standardizes the intercept, setting it and its inferential statistics to NA.
◆ Where it's heading
The package's job is to be the universal adapter for model output, so its roadmap is effectively set by what the R modelling ecosystem produces. Two threads are visible beyond coverage: getting standard errors right for awkward cases such as frailty terms and robust vcov matrices, and getting labels right when factors are converted on the fly or character variables appear in a formula. Interoperability inside easystats keeps tightening, with equivalence_test() gaining methods for modelbased objects.
◆ Prediction
Given the cadence, the next release will most likely add another model class alongside label and standard-error fixes rather than change how the package works.
Alternatives to modelbased and parameters
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 parameters.
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 parameters alternatives →
Recent activity from modelbased and parameters
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
- 1mo agoparametersparameters 0.29.2 extends lavaan support and fixes label dropping
- 2mo agoparametersparameters 0.29.1 adds a cluster argument and fixes vcov handling
- 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
- 3mo agoparametersparameters 0.29.0 stops standardizing the intercept in post-hoc methods
- 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()
- 8mo agoparametersparameters 0.28.3 adds Kenward-Roger and Satterthwaite for glmmTMB
- 11mo agoparametersparameters 0.28.2 updates tests for the latest fixest release
- 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
- 11mo agoparametersparameters 0.28.1 adds robust standard errors for glmmTMB
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
Frequently asked questions
What is the difference between modelbased and parameters?
Both compete on the same themes — easystats, mixed-models, r-package — within Analytics. modelbased and parameters 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 parameters?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. modelbased and parameters 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 parameters?
Top parameters alternatives in Analytics are ranked by recent ship velocity. Browse the "parameters alternatives" section above for the current picks, or visit /alternatives/parameters-r for the full list with editorial commentary on each.