rnaturalearth
rnaturalearth finished its sp exit and is now optimising how the data actually arrives.
Comparison · Analytics
cmdstanr vs modelbased
A side-by-side editorial comparison of cmdstanr and modelbased — release velocity, themes, recent moves, and the top alternatives to consider.
cmdstanr vs modelbased: at a glance
What is cmdstanr?
cmdstanr keeps adding fast approximations beside full HMC, and fighting Windows toolchains.
cmdstanr is the lightweight R interface to CmdStan, shelling out to the Stan binary rather than embedding it. The visible releases pair inference-method expansion, with laplace and pathfinder arriving in 0.7.0, against a continuous effort to make installation work on Windows. The most recent releases are dominated by CmdStan version compatibility and numerical fixes in the loo path.
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.
cmdstanr vs modelbased: editorial side-by-side
cmdstanr
0.0ANALYTICS
cmdstanr keeps adding fast approximations beside full HMC, and fighting Windows toolchains.
◆ Current state
cmdstanr is the lightweight R interface to CmdStan, shelling out to the Stan binary rather than embedding it. The visible releases pair inference-method expansion, with laplace and pathfinder arriving in 0.7.0, against a continuous effort to make installation work on Windows. The most recent releases are dominated by CmdStan version compatibility and numerical fixes in the loo path.
◆ Where it's heading
The package tracks CmdStan closely, and its own work concentrates in two places. One is broadening the method surface so approximate inference sits beside sampling on the same object, extended in 0.8.0 by letting a completed fit supply initial values for the next run. The other is cutting installation friction, which reaches its conclusion in 0.9.0 with RTools45 supported and no additional toolchain setup needed on Windows. Dependency trimming, such as dropping RcppEigen for direct Eigen interop, runs alongside both.
◆ Prediction
The cadence is a CmdStan release followed by a compatibility release here, so expect the next to track a newer CmdStan and continue the effective-sample-size numerical work in the loo method.
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 cmdstanr 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 cmdstanr 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 cmdstanr alternatives → · See all modelbased alternatives →
Recent activity from cmdstanr 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 agocmdstanrRTools45 support; Windows needs no extra toolchain setup
- 4mo agocmdstanrBugfix release: SUNDIALS linking, Windows paths, RTools
- 4mo agocmdstanrCmdStanFit objects usable as initial values; RcppEigen dropped
- 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
- 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
- 2y agocmdstanrBugfix release with dedicated hpp generation step
- 2y agocmdstanrLaplace and Pathfinder inference methods added ⚡
- 2y agocmdstanrjacobian argument enabled for optimization; assorted fixes
Frequently asked questions
What is the difference between cmdstanr and modelbased?
They serve adjacent needs but don't currently overlap on shipped themes. cmdstanr 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 cmdstanr better than modelbased?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cmdstanr 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 cmdstanr?
Top cmdstanr alternatives in Analytics are ranked by recent ship velocity. Browse the "cmdstanr alternatives" section above for the current picks, or visit /alternatives/cmdstanr 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.