impIndicator
Biodiversity impact indicators settle their vocabulary before 1.0
A side-by-side editorial comparison of brglm2 and worldbank — release velocity, themes, recent moves, and the top alternatives to consider.
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
A World Bank data wrapper that keeps finding the places its own API can't reach.
worldbank provides R access to the World Bank's public data: World Development Indicators, the Poverty and Inequality Platform, project records, and the Finances One datasets. It settled its type contract early — always a data.frame, never a conditional tibble — and has since layered on opt-in request caching, multi-indicator queries, and query conveniences like most-recent-values and gap filling. The most recent addition sidesteps the API entirely, pulling the full WDI archive as a zip.
brglm2 fits generalized linear models using mean and median bias reduction rather than plain maximum likelihood, which matters most when ML estimates are infinite or badly biased. The 0.7-0.9 line broadened coverage — negative binomial via brnb(), ordinal superiority measures, the expo() method for exponentiated parameters, add1()/drop1() so step() stops silently producing nonsense. Version 1.0.0 in August 2025 added mdyplFit(), estimating logistic regression by maximum Diaconis-Ylvisaker prior penalized likelihood with optional high-dimensional corrections. The two releases since have tuned that new path.
The package's older work assumed the classical regime where observations comfortably outnumber parameters. mdyplFit() and its hd_correction argument target the opposite case, and the follow-up releases are almost entirely about it — Pearson residuals on original responses, aliased parameter handling, the sloe() signal-strength estimator ignoring leverage-one observations. Meanwhile the older surface gets graceful-failure work: brglm_fit() now returns its latest estimates with warnings rather than aborting.
Given that 1.0.1 and 1.1.0 are both dominated by mdyplFit follow-ups while the classical path receives only robustness fixes, further work on high-dimensional corrections is the likeliest direction.
worldbank provides R access to the World Bank's public data: World Development Indicators, the Poverty and Inequality Platform, project records, and the Finances One datasets. It settled its type contract early — always a data.frame, never a conditional tibble — and has since layered on opt-in request caching, multi-indicator queries, and query conveniences like most-recent-values and gap filling. The most recent addition sidesteps the API entirely, pulling the full WDI archive as a zip.
Two threads run through the log. The first is query ergonomics: multiple indicators per call, mrv and gapfill parameters, regex search across the indicator catalog, a shorter wb_data() name that has since become the primary entry point. The second is coverage of things the standard API handles poorly — bulk download reaches footnote and series-time metadata the endpoints never expose, and PIP nowcasts and project records extend past the indicator tables most users start with. The maintainer runs the same infrastructure across their other data packages, and the caching design here is identical to what bbk and treasury received.
Expect the remaining rough edges of the World Bank's own API — inconsistent empty responses, metadata only available in bulk files — to keep driving releases, rather than a push into new data providers.
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 brglm2 or worldbank.
Biodiversity impact indicators settle their vocabulary before 1.0
A dormant trajectory-inference wrapper wakes up for maintenance only
The temporal half of the stscl EDM pair, tracking its spatial sibling
Spatial causal discovery in R, one exposed method per release
Shared plumbing for the Kharchenko single-cell stack, updated once a year
The R client for DataONE ships slow, correctness-focused maintenance
See all brglm2 alternatives → · See all worldbank alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. brglm2 and worldbank 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. brglm2 and worldbank 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.
Top brglm2 alternatives in Analytics are ranked by recent ship velocity. Browse the "brglm2 alternatives" section above for the current picks, or visit /alternatives/brglm2 for the full list with editorial commentary on each.
Top worldbank alternatives in Analytics are ranked by recent ship velocity. Browse the "worldbank alternatives" section above for the current picks, or visit /alternatives/worldbank for the full list with editorial commentary on each.