rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
parameters vs stacks
A side-by-side editorial comparison of parameters and stacks — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
parameters vs stacks: at a glance
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.
What is stacks?
Model stacking in tidymodels, quietly migrating off foreach and onto future
stacks builds ensembles from tidymodels tuning results, and its release history is dominated by one long project: replacing foreach-based parallelism with the future framework. That transition completed in 1.1.0, where foreach backends began being ignored with a warning and the minimum R version rose to 4.1. Releases are infrequent and small, with the most recent being a CRAN re-submission rather than a change.
parameters vs stacks: editorial side-by-side
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.
stacks
0.0ANALYTICS
Model stacking in tidymodels, quietly migrating off foreach and onto future
◆ Current state
stacks builds ensembles from tidymodels tuning results, and its release history is dominated by one long project: replacing foreach-based parallelism with the future framework. That transition completed in 1.1.0, where foreach backends began being ignored with a warning and the minimum R version rose to 4.1. Releases are infrequent and small, with the most recent being a CRAN re-submission rather than a change.
◆ Where it's heading
The package is mature and its remaining work is compatibility rather than capability — tracking the parallelism story across tidymodels, keeping object sizes sane after butchering and reloading, and staying aligned with recipes deprecations. The augment() method added for vetiver compatibility shows the same instinct: fit into the surrounding ecosystem rather than grow independently of it. Nothing in the visible history suggests new ensembling methods are being pursued.
◆ Prediction
With the future migration finished, the next release is most likely maintenance keeping pace with tune and recipes rather than anything users would notice.
Alternatives to parameters and stacks
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 parameters or stacks.
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 parameters alternatives → · See all stacks alternatives →
Recent activity from parameters and stacks
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 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 agoparametersparameters 0.29.0 stops standardizing the intercept in post-hoc methods
- 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 agoparametersparameters 0.28.1 adds robust standard errors for glmmTMB
- 1y agostacksstacks 1.1.1 re-released to clear a CRAN check note
- 1y agostacksstacks 1.1.0 completes the move to future-based parallelism
- 2y agostacksstacks 1.0.5 fixes butchered stack size inflation
- 2y agostacksstacks 1.0.4 introduces future-based parallel processing
- 2y agostacksstacks 1.0.3 clears recipes deprecations and a type-check bug
- 3y agostacksstacks 1.0.2 adds an augment() method for vetiver compatibility
Frequently asked questions
What is the difference between parameters and stacks?
Both compete on the same themes — r-package — within Analytics. parameters and stacks 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 parameters better than stacks?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. parameters and stacks 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 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.
What are the best alternatives to stacks?
Top stacks alternatives in Analytics are ranked by recent ship velocity. Browse the "stacks alternatives" section above for the current picks, or visit /alternatives/stacks for the full list with editorial commentary on each.