compositional.mle
An MLE package rebuilt around composable solvers, then renamed to match.
A side-by-side editorial comparison of cyclocomp and modelbpp — release velocity, themes, recent moves, and the top alternatives to consider.
A cyclomatic complexity checker that ships once every couple of years, and lands when it does.
cyclocomp measures cyclomatic complexity of R functions and packages, and is best known as the engine behind lintr's complexity rule. It has three releases in the visible window spread across nearly three years. The current one, 1.1.2, adds a quiet argument to cyclocomp_package_dir(); the two before it, shipped a day apart in 2023, added a large speedup and the package-directory entry point itself.
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.
cyclocomp measures cyclomatic complexity of R functions and packages, and is best known as the engine behind lintr's complexity rule. It has three releases in the visible window spread across nearly three years. The current one, 1.1.2, adds a quiet argument to cyclocomp_package_dir(); the two before it, shipped a day apart in 2023, added a large speedup and the package-directory entry point itself.
This is a small tool that reached feature-complete and now moves only when a downstream consumer needs something. Every change in the window is externally contributed, and each addresses a concrete integration need: a function that works on a local package tree rather than an installed one, complexity results sorted so the worst offenders come first, and output suppression for programmatic callers. The 2023 pair shipped a day apart because the new entry point immediately exposed a performance problem on long linear code.
Expect the next change to arrive the same way, as a contributed patch serving a linting or CI workflow rather than as planned development.
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 cyclocomp 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.
Extreme value sampling in pure upkeep mode, mostly answering to Rcpp and CRAN.
A package retired in 2017 just got rewritten against R's public C API.
See all cyclocomp 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 cyclocomp alternatives in Analytics are ranked by recent ship velocity. Browse the "cyclocomp alternatives" section above for the current picks, or visit /alternatives/cyclocomp 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.