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kde1d vs mev

A side-by-side editorial comparison of kde1d and mev — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

kde1d vs mev: at a glance

Featurekde1dmev
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesdensity-estimation, kernel-methods, zero-inflation, cpp-libraryextreme-value-theory, threshold-selection, statistical-estimation, api-redesign
Last editorial update1h ago2h ago
WebsiteVisit →Visit →

What is kde1d?

A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.

kde1d estimates univariate densities with local polynomial kernel methods, handling bounded, discrete and now zero-inflated variables through a single type argument, with the numerical work in a header-only C++ library usable outside R. Version 1.1.0 added the zero-inflated discrete-continuous mixture case and shipped a new C++ API as an explicit breaking change; 1.1.1 followed in June with auto-generated notes and no description.

Read the full kde1d trajectory →

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 →

kde1d vs mev: editorial side-by-side

K
kde1d
ANALYTICS
0.0

A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.

◆ Current state

kde1d estimates univariate densities with local polynomial kernel methods, handling bounded, discrete and now zero-inflated variables through a single type argument, with the numerical work in a header-only C++ library usable outside R. Version 1.1.0 added the zero-inflated discrete-continuous mixture case and shipped a new C++ API as an explicit breaking change; 1.1.1 followed in June with auto-generated notes and no description.

◆ Where it's heading

The package has alternated between performance work and widening the class of data it accepts. The 1.0.0 release was the performance milestone — FFT-based estimation, a better integration algorithm for the p, q and r functions, deterministic jittering replacing randomness, and standalone C++ headers. The 1.1.0 release is the scope milestone, adding a third data type to the two it already handled. Releases come from the same maintainer as svines and cluster on shared dates, so changes in the underlying C++ surface across the vine and density stack tend to ship together.

◆ Prediction

With the C++ API deliberately reworked for standalone use at 1.1.0, further work most plausibly consolidates that interface rather than adding data types. What 1.1.1 actually changed is not readable from its body.

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.

Alternatives to kde1d and mev

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 kde1d or mev.

See all kde1d alternatives → · See all mev alternatives →

Recent activity from kde1d and mev

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 agokde1dkde1d 1.1.1
  4. 1y agokde1dZero-inflated mixtures and a new standalone C++ API
  5. 2y agomevBoundary-case likelihood fixes, bundled with the prior release's notes
  6. 3y agomevGEV and GP distribution functions brought in-house to drop evd
  7. 4y agomevFour max-stable families, fixed parameters and threshold diagnostics
  8. 4y agokde1dBit-wise Boolean operations removed
  9. 5y agokde1ddkde1d() invisible output fixed
  10. 5y agokde1dValgrind false positive silenced
  11. 6y agokde1dqrng dependency dropped; undefined behaviour fixed

Frequently asked questions

What is the difference between kde1d and mev?

Both compete on the same themes — r-package — within Analytics. kde1d and mev 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 kde1d better than mev?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. kde1d and mev 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 kde1d?

Top kde1d alternatives in Analytics are ranked by recent ship velocity. Browse the "kde1d alternatives" section above for the current picks, or visit /alternatives/kde1d for the full list with editorial commentary on each.

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.