compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of hubExamples and modelbpp — release velocity, themes, recent moves, and the top alternatives to consider.
Example data for the hubverse, moving whenever the standards it demonstrates move.
hubExamples ships the reference datasets that hubverse vignettes and downstream packages use to demonstrate forecast and target data. Its releases track the hubverse specification rather than any independent roadmap: 1.0.0 exists because the target time series standard changed, and 0.1.0 because the oracle output terminology did. The current release, 1.0.1, is a single fix for column deserialisation on systems without arrow.
A structural-equation model comparison package whose feed carries links, not release notes.
modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.
hubExamples ships the reference datasets that hubverse vignettes and downstream packages use to demonstrate forecast and target data. Its releases track the hubverse specification rather than any independent roadmap: 1.0.0 exists because the target time series standard changed, and 0.1.0 because the oracle output terminology did. The current release, 1.0.1, is a single fix for column deserialisation on systems without arrow.
This is a downstream member of the hubverse package family, alongside hubUtils, hubData and hubValidations, and it moves when they define something new. The pattern across all four entries is the same: a standard changes upstream, hubExamples updates its data objects and vignettes to match. Sibling package hubAdmin has not shipped since November 2025, so the cohort is not currently in a coordinated wave.
The next release will most likely follow the next hubverse data-standard revision rather than lead it.
modelbpp computes model-implied Bayesian posterior probabilities for structural equation models, one of several R packages from the same author covering moderation, mediation and model-comparison workflows. Its release feed is not a changelog: every entry points at the package website rather than describing what changed, so the substance of each release is not visible here. Version numbering has moved steadily from 0.1.x to 0.4.0 across roughly three years.
What can be read from this feed is cadence rather than content — releases clustered noticeably more tightly through 2026 than in the preceding two years, with three in five months against two in the prior eighteen. Because the entries carry no detail, any statement about what is being built would be speculation. The pattern of a stable CRAN package accelerating its release rate is the only reliable signal available.
The feed does not describe its changes, so the direction of development cannot be read from these entries; the accelerating 2026 cadence is the only thing it supports.
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 hubExamples or modelbpp.
An MLE package rebuilt around composable solvers, then renamed to match.
nabla dropped its C++ engine to chase exact derivatives at any order.
Eight months from first release to keyring caching and workload identity.
A research-project workflow package where the interesting work is in the plumbing.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
See all hubExamples alternatives → · See all modelbpp alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. modelbpp 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. modelbpp 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 hubExamples alternatives in Analytics are ranked by recent ship velocity. Browse the "hubExamples alternatives" section above for the current picks, or visit /alternatives/hubexamples for the full list with editorial commentary on each.
Top modelbpp alternatives in Analytics are ranked by recent ship velocity. Browse the "modelbpp alternatives" section above for the current picks, or visit /alternatives/modelbpp for the full list with editorial commentary on each.