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 tEDM — 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.
The temporal half of the stscl EDM pair, tracking its spatial sibling
tEDM applies empirical dynamic modeling to time series — cross mapping, convergent cross mapping and the logistic map — as the temporal counterpart to spEDM, with which it shares a maintainer and a C++ core. The recent releases are consolidation rather than expansion: index handling in cross mapping corrected, generics taught to accept varying E, k and tau, and the associated paper now cited in the README. Only three releases are visible in the feed.
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.
tEDM applies empirical dynamic modeling to time series — cross mapping, convergent cross mapping and the logistic map — as the temporal counterpart to spEDM, with which it shares a maintainer and a C++ core. The recent releases are consolidation rather than expansion: index handling in cross mapping corrected, generics taught to accept varying E, k and tau, and the associated paper now cited in the README. Only three releases are visible in the feed.
tEDM moves in lockstep with spEDM. Configurable distance metrics, varying E/k/tau inputs, strict floating-point comparison and the S3 plotting font unification all appear in both packages within days or weeks, as does the maintainer surname correction. The recent balance has tilted toward correcting library and prediction index handling — the kind of repeated attention that suggests the indexing model was the weak point of the shared core.
Expect tEDM to keep inheriting the shared-core changes spEDM lands, with its own releases staying small and centred on cross-mapping parameter handling rather than new method surface.
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 tEDM.
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.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. mev and tEDM 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 tEDM 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 tEDM alternatives in Analytics are ranked by recent ship velocity. Browse the "tEDM alternatives" section above for the current picks, or visit /alternatives/tedm for the full list with editorial commentary on each.