simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of simmer.plot and svines — release velocity, themes, recent moves, and the top alternatives to consider.
The plotting companion to simmer, shipping only when the simulator or a graphics dependency moves.
simmer.plot renders discrete-event simulation output — S3 plot() methods over get_mon_arrivals(), get_mon_attributes() and get_mon_resources(), plus trajectory diagrams drawn through DiagrammeR. Since 0.1.12 the methods attach to the monitoring data itself rather than the simulation environment, and 0.1.18 finished that migration by deleting the deprecated environment-level methods.
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.
simmer.plot renders discrete-event simulation output — S3 plot() methods over get_mon_arrivals(), get_mon_attributes() and get_mon_resources(), plus trajectory diagrams drawn through DiagrammeR. Since 0.1.12 the methods attach to the monitoring data itself rather than the simulation environment, and 0.1.18 finished that migration by deleting the deprecated environment-level methods.
This package moves when something it depends on moves. Its history is a sequence of parser fixes for new simmer trajectory formats, DiagrammeR and tidyr and dplyr version bumps, and ggplot2 workarounds. The one clear internal decision — plotting monitor output instead of the environment — was made in 2017 and completed six years later. The 2025 release fixes documentation cross-references and nothing else.
The next release most likely follows a simmer trajectory-format change or a CRAN documentation policy, matching every recent entry. There is no visible feature work in the pipeline.
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 simmer.plot 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 simmer.plot alternatives → · See all svines alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. simmer.plot 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. simmer.plot 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 simmer.plot alternatives in Analytics are ranked by recent ship velocity. Browse the "simmer.plot alternatives" section above for the current picks, or visit /alternatives/simmer-plot 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.