rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
forecast vs modelbased
A side-by-side editorial comparison of forecast and modelbased — release velocity, themes, recent moves, and the top alternatives to consider.
forecast vs modelbased: at a glance
What is forecast?
After years of pure maintenance, forecast 9.0.0 reopens the package
forecast is the long-established R forecasting package that fable was meant to succeed. For several years its releases were RNG fixes, base-R compatibility and documentation. Then 9.0.0 arrived with a batch of new model constructors, wider prediction-interval support and a rewritten accuracy() built on S3 methods.
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.
forecast vs modelbased: editorial side-by-side
forecast
0.0ANALYTICS
After years of pure maintenance, forecast 9.0.0 reopens the package
◆ Current state
forecast is the long-established R forecasting package that fable was meant to succeed. For several years its releases were RNG fixes, base-R compatibility and documentation. Then 9.0.0 arrived with a batch of new model constructors, wider prediction-interval support and a rewritten accuracy() built on S3 methods.
◆ Where it's heading
The major version reframes forecast around explicit *_model() constructors — mean, random walk, spline, theta, Croston — rather than the older function-per-method style, and the 9.0.x patches since have been performance and argument-handling cleanups on top. That is an active maintenance line, not a package winding down in favour of fable.
◆ Prediction
Expect continued 9.0.x patches consolidating the new constructors and their forecast methods, with the older interfaces kept working alongside them.
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 forecast 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 forecast 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 forecast alternatives → · See all modelbased alternatives →
Recent activity from forecast 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
- 4mo agoforecastFixes for checkresiduals() and mstl() lambda handling
- 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
- 5mo agoforecastFaster ARFIMA search and forecast.mlm() argument handling
- 7mo agoforecastforecast 9.0.0 adds five model constructors and rewrites accuracy() ⚡
- 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
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
- 1y agoforecastDocumentation and bug-fix release
- 2y agoforecastRNG state and base-R head/tail compatibility
- 2y agoforecastMuch faster hfitted() for ARIMA and ETS models
Frequently asked questions
What is the difference between forecast and modelbased?
They serve adjacent needs but don't currently overlap on shipped themes. forecast 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 forecast better than modelbased?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. forecast 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 forecast?
Top forecast alternatives in Analytics are ranked by recent ship velocity. Browse the "forecast alternatives" section above for the current picks, or visit /alternatives/forecast-r 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.