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mev vs vinereg

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

Shared themes:r-package

mev vs vinereg: at a glance

Featuremevvinereg
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesextreme-value-theory, threshold-selection, statistical-estimation, api-redesignr-package, copulas, regression, conditional-density
Last editorial update2h ago52m ago
WebsiteVisit →Visit →

What is mev?

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.

Read the full mev trajectory →

What is vinereg?

Conditional density and log-likelihood fill out a vine copula regression package.

vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.

Read the full vinereg trajectory →

mev vs vinereg: editorial side-by-side

M
mev
ANALYTICS
0.0

An extreme-value toolkit reorganised its whole API into prefixed families and tripled its estimator count.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

V
vinereg
ANALYTICS
0.0

Conditional density and log-likelihood fill out a vine copula regression package.

◆ Current state

vinereg fits D-vine copula-based regression models on top of rvinecopulib and kde1d, in Thomas Nagler's package stack. The January 2025 pair - 0.10.0 and 0.11.0 tagged the same day - adds a pdf() function and then fixes conditional density computation for discrete variables while requiring the newer kde1d. Release notes run to one or two bullets each.

◆ Where it's heading

Work has concentrated on evaluation rather than fitting: cll() in 0.9.0, pdf() in 0.10.0, and the discrete-variable correction in 0.11.0 all concern what can be computed from a model already fitted. Releases arrive in same-day pairs, and the notes are terse enough that 0.10.0 reuses 0.9.0's wording verbatim, describing pdf() with cll()'s sentence. Version floors also track the sibling packages - kde1d here, rvinecopulib in 0.8.3.

◆ Prediction

Given the shared release rhythm across the stack, the next entry is as likely to be a dependency-driven bump as a new function; the discrete-variable path is the one area these notes show as recently unstable.

Alternatives to mev and vinereg

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 vinereg.

See all mev alternatives → · See all vinereg alternatives →

Recent activity from mev and vinereg

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

  1. 9mo agomevTwo more threshold-selection routines slot into the new scheme
  2. 9mo agomevThreshold, stability and dependence functions regrouped under prefixes
  3. 1y agovineregDiscrete conditional densities fixed; kde1d 1.1.0 required
  4. 1y agovineregpdf() added for conditional density
  5. 2y agomevBoundary-case likelihood fixes, bundled with the prior release's notes
  6. 2y agovineregBoost compile flag and a weights error fixed
  7. 2y agovineregcll() computes conditional log-likelihood
  8. 3y agomevGEV and GP distribution functions brought in-house to drop evd
  9. 4y agomevFour max-stable families, fixed parameters and threshold diagnostics
  10. 4y agovineregvinecopulib floor raised for RcppThread compatibility
  11. 4y agovineregcpit() fixed and external marginals allowed via uscale

Frequently asked questions

What is the difference between mev and vinereg?

Both compete on the same themes — r-package — within Analytics. mev and vinereg 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 mev better than vinereg?

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

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.

What are the best alternatives to vinereg?

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