nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
A side-by-side editorial comparison of bayestools and moderndive — 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.
The ModernDive teaching package learns to render inside the browser that runs its own textbook
moderndive supplies the datasets, regression helpers and ggplot geoms used by the ModernDive introductory statistics textbook. Its release history is mostly dataset accumulation — much of it contributed by students in batches — punctuated by occasional function work. The latest release is different: it fixes View() so it renders inside webR, the in-browser R that powers the book's live exercises, and reworks the regression helpers to survive in-formula transformations.
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.
moderndive supplies the datasets, regression helpers and ggplot geoms used by the ModernDive introductory statistics textbook. Its release history is mostly dataset accumulation — much of it contributed by students in batches — punctuated by occasional function work. The latest release is different: it fixes View() so it renders inside webR, the in-browser R that powers the book's live exercises, and reworks the regression helpers to survive in-formula transformations.
The package is following the textbook's second edition into the browser. webR has no pandoc, so the DT htmlwidget path the package relied on cannot produce the self-contained HTML the notebook cell needs, and the auto-print path was gated behind interactive() being false — meaning students working through the live exercises saw an explanatory message where a table should have been. Building a static HTML table and pushing it through webR's viewer hook is a small change with a direct effect on whether the book's interactive mode works at all.
With the book's v2 datasets landed and the browser rendering path fixed, the remaining friction is most likely in other functions that assume a desktop R session.
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 moderndive.
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
See all bayestools alternatives → · See all moderndive alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. moderndive 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. moderndive 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 moderndive alternatives in Analytics are ranked by recent ship velocity. Browse the "moderndive alternatives" section above for the current picks, or visit /alternatives/moderndive for the full list with editorial commentary on each.