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svines vs vinereg

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

Shared themes:r-package

svines vs vinereg: at a glance

Featuresvinesvinereg
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesvine-copulas, time-series, dependence-modelling, rcppr-package, copulas, regression, conditional-density
Last editorial update1h ago50m ago
WebsiteVisit →Visit →

What is svines?

Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.

svines fits stationary vine copula models to multivariate time series, extending the rvinecopulib engine with the serial dependence structure that makes vines usable for temporal data. The visible history is three releases carrying one real addition — pseudo-residual computation and logLik support at 0.2.2 — with the rest tracking its C++ dependency.

Read the full svines 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 →

svines vs vinereg: editorial side-by-side

S
svines
ANALYTICS
0.0

Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.

◆ Current state

svines fits stationary vine copula models to multivariate time series, extending the rvinecopulib engine with the serial dependence structure that makes vines usable for temporal data. The visible history is three releases carrying one real addition — pseudo-residual computation and logLik support at 0.2.2 — with the rest tracking its C++ dependency.

◆ Where it's heading

This package moves when rvinecopulib moves. The 0.2.4 release exists solely to adapt to a new rvinecopulib version, and 0.2.7 carries auto-generated GitHub release notes with no description at all. It shipped on the same day as kde1d 1.1.1, another package from the same maintainer, which is the pattern to watch: changes in the shared C++ layer surface as near-simultaneous releases across the vine family rather than as independent work.

◆ Prediction

The next release most plausibly follows another rvinecopulib update rather than adding modelling capability. Two of the three visible entries carry no substantive notes, so this feed will keep underreporting what changed.

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 svines 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 svines or vinereg.

See all svines alternatives → · See all vinereg alternatives →

Recent activity from svines and vinereg

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

  1. 1y agosvinessvines 0.2.7
  2. 1y agosvinesAdapted to new rvinecopulib version
  3. 1y agovineregDiscrete conditional densities fixed; kde1d 1.1.0 required
  4. 1y agovineregpdf() added for conditional density
  5. 2y agovineregBoost compile flag and a weights error fixed
  6. 2y agosvinesPseudo residuals and logLik support added
  7. 2y agovineregcll() computes conditional log-likelihood
  8. 4y agovineregvinecopulib floor raised for RcppThread compatibility
  9. 4y agovineregcpit() fixed and external marginals allowed via uscale

Frequently asked questions

What is the difference between svines and vinereg?

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

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

Top svines alternatives in Analytics are ranked by recent ship velocity. Browse the "svines alternatives" section above for the current picks, or visit /alternatives/svines 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.