fillpattern
Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of bayestools and fitVARMxID — release velocity, themes, recent moves, and the top alternatives to consider.
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
BayesTools provides the shared JAGS fitting, prior and summary infrastructure that the author's meta-analysis packages build on. The 0.2.x line filled in modeling primitives — prior_mixture() and mixed-posterior objects in 0.2.18, expression-valued priors and lme4-style uncorrelated random effects in 0.2.20, then a run of small diagnostic fixes for mixture and spike-and-slab priors. Version 0.3.0 in May 2026 adds automatic standardization of continuous predictors, default priors for unspecified factor and continuous terms, and functions to transform prior and posterior samples back to the original scale.
A VAR-model fitting package acquiring the standard R methods it launched without
fitVARMxID fits vector autoregressive models via OpenMx identification, and is one of several packages maintained by the jeksterslab account. Its recent releases are small and additive: confint() and plot() methods, a save function, and before that a documentation pass. The feed also carries automated build commits that are pure CI artifacts.
BayesTools provides the shared JAGS fitting, prior and summary infrastructure that the author's meta-analysis packages build on. The 0.2.x line filled in modeling primitives — prior_mixture() and mixed-posterior objects in 0.2.18, expression-valued priors and lme4-style uncorrelated random effects in 0.2.20, then a run of small diagnostic fixes for mixture and spike-and-slab priors. Version 0.3.0 in May 2026 adds automatic standardization of continuous predictors, default priors for unspecified factor and continuous terms, and functions to transform prior and posterior samples back to the original scale.
This package's releases are best read against what depends on them. The 0.2.x fixes track features appearing in RoBMA one version later, and 0.3.0 landed a single day before RoBMA 4.0.0 — the standardization and sample-transformation functions are the substrate that rewrite needed. The direction of the work is toward sensible defaults: default priors by predictor type, automatic standardization for sampling stability, and transformation back to interpretable scale so the convenience does not cost the user their units.
Given how tightly its releases track downstream needs, the next version is most likely driven by gaps surfacing in RoBMA 4.0.x rather than by independent feature work.
fitVARMxID fits vector autoregressive models via OpenMx identification, and is one of several packages maintained by the jeksterslab account. Its recent releases are small and additive: confint() and plot() methods, a save function, and before that a documentation pass. The feed also carries automated build commits that are pure CI artifacts.
This is a package settling into R conventions rather than growing capability. Adding confint() and plot() is the standard-methods work most modeling packages do once the estimation core is stable — it signals the author considers the fitting side done. Cadence is roughly quarterly and the changes get smaller each time.
Further method coverage — summary(), predict(), or coef() — is the likely next step, since confint() and plot() are usually the first two of that set rather than the last.
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 bayestools or fitVARMxID.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
See all bayestools alternatives → · See all fitVARMxID alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fitVARMxID is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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. fitVARMxID is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top bayestools alternatives in Analytics are ranked by recent ship velocity. Browse the "bayestools alternatives" section above for the current picks, or visit /alternatives/bayestools for the full list with editorial commentary on each.
Top fitVARMxID alternatives in Analytics are ranked by recent ship velocity. Browse the "fitVARMxID alternatives" section above for the current picks, or visit /alternatives/fitvarmxid for the full list with editorial commentary on each.