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Comparison · Analytics

modelbased vs rgbif

A side-by-side editorial comparison of modelbased and rgbif — release velocity, themes, recent moves, and the top alternatives to consider.

modelbased vs rgbif: at a glance

Featuremodelbasedrgbif
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themeseasystats, marginal-effects, contrasts, mixed-modelsgbif, biodiversity-data, bulk-downloads, sql-queries
Last editorial update1h ago53m ago
WebsiteVisit →Visit →

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.

Read the full modelbased trajectory →

What is rgbif?

rgbif is steadily pushing users off paged searching and onto real downloads.

rgbif ships several times a year and the recent releases cluster around downloads. 3.8.1 added `occ_download_sql()` for SQL-based occurrence downloads; 3.8.2 added DOI-to-download-key resolution and institutionKey downloads; 3.8.3 relayed GBIF's new throttling of bulk paging through `occ_search()` with a message pointing users at `occ_download()`; 3.8.5 added a family of `occ_download_stats_*()` functions plus multiple-taxonomy and verbatim-extension downloads. Alongside that, `name_backbone()` moved to GBIF API v2, and 3.8.0 soft-deprecated `occ_data()`.

Read the full rgbif trajectory →

modelbased vs rgbif: editorial side-by-side

M
modelbased
ANALYTICS
0.0

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.

R
rgbif
ANALYTICS
0.0

rgbif is steadily pushing users off paged searching and onto real downloads.

◆ Current state

rgbif ships several times a year and the recent releases cluster around downloads. 3.8.1 added `occ_download_sql()` for SQL-based occurrence downloads; 3.8.2 added DOI-to-download-key resolution and institutionKey downloads; 3.8.3 relayed GBIF's new throttling of bulk paging through `occ_search()` with a message pointing users at `occ_download()`; 3.8.5 added a family of `occ_download_stats_*()` functions plus multiple-taxonomy and verbatim-extension downloads. Alongside that, `name_backbone()` moved to GBIF API v2, and 3.8.0 soft-deprecated `occ_data()`.

◆ Where it's heading

Two things are happening at once. GBIF is discouraging bulk retrieval through the search API, and rgbif is building out the download path fast enough to absorb the traffic — SQL queries, DOI round-tripping, format description, and now statistics about the downloads themselves. Metadata coverage has expanded in parallel, with a dozen `dataset_*()` functions in 3.7.9 and GRSciColl institution search in 3.8.3. The deprecations are consistent: `occ_data()`, `occ_facet()`, `occ_count(type=)` all retired in favour of narrower replacements.

◆ Prediction

The download surface is where the next additions will land — likely more SQL-download tooling and further statistics endpoints, following 3.8.1 and 3.8.5.

Alternatives to modelbased and rgbif

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 rgbif.

See all modelbased alternatives → · See all rgbif alternatives →

Recent activity from modelbased and rgbif

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agomodelbasedmodelbased 0.16.0 adds post-processing and omnibus contrast tests
  2. 3mo agomodelbasedmodelbased 0.15.0 contrasts average slopes across numeric predictors
  3. 4mo agorgbifrgbif 3.8.5 adds occurrence download statistics functions
  4. 5mo agomodelbasedmodelbased 0.14.0 renames trend to slope and adds collapse_by_group()
  5. 8mo agomodelbasedmodelbased 0.13.1 adds marginal group-level estimates and as.data.frame()
  6. 8mo agorgbifrgbif 3.8.4 moves name matching to GBIF API v2
  7. 11mo agorgbifrgbif 3.8.3 adds GRSciColl search, relays GBIF paging throttle
  8. 11mo agomodelbasedmodelbased 0.13.0 adds inequality ratios and slope marginalization
  9. 1y agomodelbasedmodelbased 0.12.0 introduces marginal effects inequality measures
  10. 1y agorgbifrgbif 3.8.2 resolves download DOIs to keys
  11. 1y agorgbifrgbif 3.8.1 adds SQL-based occurrence downloads
  12. 2y agorgbifrgbif 3.8.0 soft-deprecates occ_data(), adds download describe

Frequently asked questions

What is the difference between modelbased and rgbif?

They serve adjacent needs but don't currently overlap on shipped themes. modelbased and rgbif 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 rgbif?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. modelbased and rgbif 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 rgbif?

Top rgbif alternatives in Analytics are ranked by recent ship velocity. Browse the "rgbif alternatives" section above for the current picks, or visit /alternatives/rgbif for the full list with editorial commentary on each.