Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
A side-by-side editorial comparison of climaemet and distributions3 — release velocity, themes, recent moves, and the top alternatives to consider.
climaemet added weather alerts and wildfire risk, then spent two years managing rate limits.
climaemet wraps Spain's AEMET meteorological API — station data, historical climate series, forecasts, and the plotting helpers that go with them. Its capability surface widened decisively in 1.4.0 with meteorological alerts and wildfire risk rasters. Everything since has been about surviving the API rather than extending it: multiple API keys, quota-aware key selection, and httr2 throttling pinned to AEMET's stated 40-connections-per-minute policy.
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
climaemet wraps Spain's AEMET meteorological API — station data, historical climate series, forecasts, and the plotting helpers that go with them. Its capability surface widened decisively in 1.4.0 with meteorological alerts and wildfire risk rasters. Everything since has been about surviving the API rather than extending it: multiple API keys, quota-aware key selection, and httr2 throttling pinned to AEMET's stated 40-connections-per-minute policy.
Two forces shape this package, and neither is feature demand. The first is AEMET's own churn — new response codes, a fires endpoint that switched to six risk levels returned as named factors, municipality datasets refreshed annually. The second is the maintainer's cross-package modernization, visible here as the API key store moving to tools::R_user_dir() with automatic migration, a configurable timeout, cli messaging, and an R 4.1 floor. The 1.6.0 refactor is stated as AI-assisted, matching the maintainer's other packages.
Expect the next release to track another AEMET endpoint change rather than add a data domain; the throttling and multi-key machinery suggests quota pressure is the constraint the maintainer keeps returning to.
An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.
Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().
With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.
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 climaemet or distributions3.
Basedash keeps pushing its data out of the workspace — now to people without accounts
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See all climaemet alternatives → · See all distributions3 alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. 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. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top climaemet alternatives in Analytics are ranked by recent ship velocity. Browse the "climaemet alternatives" section above for the current picks, or visit /alternatives/climaemet for the full list with editorial commentary on each.
Top distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.