TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of mev and soilDBdata — release velocity, themes, recent moves, and the top alternatives to consider.
An extreme-value toolkit reorganised its whole API into prefixed families and tripled its estimator count.
mev provides likelihood-based inference for univariate and multivariate extreme value models — threshold selection, shape estimation, tail dependence and max-stable simulation. Version 2.0 was a deliberate reorganisation: every threshold-selection routine now carries a thselect. prefix, every stability plot a tstab. prefix, and every extremal-dependence measure an xdep. prefix, with the old names deprecated but mostly still working. The same release added a large batch of estimators — Stein-weighted GPD, roughly a dozen shape estimators, second-order regular variation, L-moment GPD and Weissman quantiles.
soilDBdata exists so soilDB's tests can run without a NASIS connection.
soilDBdata is a data-only package supplying NASIS and gSSURGO sample datasets as .sqlite assets, installed separately by soilDB's GitHub Actions so unit tests that would otherwise need database access can run. It began as a proof of concept carrying MT663 pedon and component tables used in soil survey coursework, and its most recent release adds a Marshall Islands FY26 gSSURGO dataset. Releases are infrequent and driven by what the parent package needs to test.
mev provides likelihood-based inference for univariate and multivariate extreme value models — threshold selection, shape estimation, tail dependence and max-stable simulation. Version 2.0 was a deliberate reorganisation: every threshold-selection routine now carries a thselect. prefix, every stability plot a tstab. prefix, and every extremal-dependence measure an xdep. prefix, with the old names deprecated but mostly still working. The same release added a large batch of estimators — Stein-weighted GPD, roughly a dozen shape estimators, second-order regular variation, L-moment GPD and Weissman quantiles.
The package is consolidating into a reference implementation of the extreme-value literature rather than a collection of one-off routines. Sixteen threshold-selection methods now share standardised arguments and their own plot and print methods with automatic selection, which is the tell: the goal is comparability across methods, not just availability. Dependency reduction runs alongside, with distribution functions written in-package to drop evd and Rsolnp replacing nloptr in earlier releases.
Version 2.1 continued adding threshold-selection routines within the new naming scheme, so the next release most likely follows the same pattern — more estimators fitted to the established prefixes, plus fixes to the 2.0 renaming. The entries give no sign of a further structural change.
soilDBdata is a data-only package supplying NASIS and gSSURGO sample datasets as .sqlite assets, installed separately by soilDB's GitHub Actions so unit tests that would otherwise need database access can run. It began as a proof of concept carrying MT663 pedon and component tables used in soil survey coursework, and its most recent release adds a Marshall Islands FY26 gSSURGO dataset. Releases are infrequent and driven by what the parent package needs to test.
Development follows soilDB rather than leading it: assets get bumped when a soilDB version changes, and purpose lists are updated when soilDB adds a table. The one release that changed what testing is possible was v0.1.1, which added selected-set _View_1 tables alongside whole tables so both SS=TRUE and SS=FALSE code paths could be exercised. Four-year gaps between releases are normal here and do not indicate abandonment — a fixture package only needs to move when the fixtures go stale.
The recent addition is a new geography rather than a new table structure, so further releases most likely continue broadening dataset coverage as soilDB gains regions to test against.
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 mev or soilDBdata.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
See all mev alternatives → · See all soilDBdata alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. mev and soilDBdata 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. mev and soilDBdata 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 mev alternatives in Analytics are ranked by recent ship velocity. Browse the "mev alternatives" section above for the current picks, or visit /alternatives/mev for the full list with editorial commentary on each.
Top soilDBdata alternatives in Analytics are ranked by recent ship velocity. Browse the "soilDBdata alternatives" section above for the current picks, or visit /alternatives/soildbdata for the full list with editorial commentary on each.