simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of splines2 and svines — release velocity, themes, recent moves, and the top alternatives to consider.
Spline bases built to interoperate: periodic B-splines and an nsk-compatible natural basis.
splines2 provides spline basis functions with their derivatives and integrals, in R and through an Rcpp interface. The 0.5.0 release in mid-2023 set the package's current surface; the four releases since are a correctness fix for natural cubic splines with one internal knot, a plotting argument, a compiler warning, and a documentation repair.
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.
splines2 provides spline basis functions with their derivatives and integrals, in R and through an Rcpp interface. The 0.5.0 release in mid-2023 set the package's current surface; the four releases since are a correctness fix for natural cubic splines with one internal knot, a plotting argument, a compiler warning, and a documentation repair.
The direction is interoperability rather than new mathematics. 0.5.0 added nsk() to match survival::nsk(), an H matrix for converting cubic B-splines produced elsewhere into this package's natural splines, and short aliases meant to be typed inside model formulas. Periodic B-splines were the one genuinely new basis, and its Rcpp knot-sequence handling needed a follow-up fix. Wenjie Wang maintains it alongside intsurv and reda.
The last four releases are all corrections, so the next one most likely continues that pattern; a further basis type would break a two-year run of consolidation.
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.
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.
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.
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 splines2 or svines.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
Conditional density and log-likelihood fill out a vine copula regression package.
A drop-in string API for base R, kept alive by upstream check failures.
See all splines2 alternatives → · See all svines alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package, rcpp — within Analytics. splines2 and svines 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. splines2 and svines 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 splines2 alternatives in Analytics are ranked by recent ship velocity. Browse the "splines2 alternatives" section above for the current picks, or visit /alternatives/splines2 for the full list with editorial commentary on each.
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.