JointFPM
Recurrent-event modelling settles, with mean_no() promoted to stable.
A side-by-side editorial comparison of neonUtilities and tidyplots — release velocity, themes, recent moves, and the top alternatives to consider.
Two major versions shipped in a year, and this feed will not say what changed in either.
neonUtilities is the R toolkit NEON publishes for pulling and assembling its own observatory data — downloading data products through the NEON API, unzipping and stacking monthly packages into analysis-ready tables, and handling the awkward cases like eddy-covariance and airborne data. It reached 4.0.0 in June and 4.0.1 in July. What those releases contain is not recoverable from this feed: every recent entry is a one-line pointer saying the tag corresponds to a CRAN version, with the change log left in NEWS.md.
tidyplots keeps rebuilding its own foundations rather than layering around them.
tidyplots wraps ggplot2 in a pipe-driven API aimed at publication-ready scientific figures, trading grammar-of-graphics flexibility for a shorter path to a finished plot. It is at 0.4.0 after two years of frequent releases, and almost every one carries a breaking change — the most recent moved multi-panel layout off patchwork and onto ggplot2's own faceting. Statistical annotation, colour schemes and size control have each been reworked at least once.
neonUtilities is the R toolkit NEON publishes for pulling and assembling its own observatory data — downloading data products through the NEON API, unzipping and stacking monthly packages into analysis-ready tables, and handling the awkward cases like eddy-covariance and airborne data. It reached 4.0.0 in June and 4.0.1 in July. What those releases contain is not recoverable from this feed: every recent entry is a one-line pointer saying the tag corresponds to a CRAN version, with the change log left in NEWS.md.
Release cadence has picked up sharply — 3.0.0 through 4.0.1 in under a year, against multi-year gaps before that — and two major-version bumps in that window normally imply breaking changes for anyone pinning the package in a reproducible workflow. Direction cannot be read from the entries themselves. The one substantive note in the feed is older and instructive about how this repository is used: a 2023 development tag that modified stackEddy() to avoid NEON API calls for internal processing pipelines, explicitly not for public use and never submitted to CRAN.
No prediction is supportable from these entries — they contain no description of any change. What can be said is that the 3.x-to-4.x jump and the tight 4.0.0-to-4.0.1 turnaround fit the usual shape of a major release followed by a fix, and anyone depending on the package should read NEWS.md rather than this feed.
tidyplots wraps ggplot2 in a pipe-driven API aimed at publication-ready scientific figures, trading grammar-of-graphics flexibility for a shorter path to a finished plot. It is at 0.4.0 after two years of frequent releases, and almost every one carries a breaking change — the most recent moved multi-panel layout off patchwork and onto ggplot2's own faceting. Statistical annotation, colour schemes and size control have each been reworked at least once.
The package is converging on ggplot2 rather than abstracting away from it: split_plot() now uses facet_wrap and facet_grid, as_tidyplot() was hard-deprecated on the grounds that converting a ggplot was never a good idea, and releases are timed against upstream ggplot2 versions. The other constant is the statistics surface, which has grown from basic error bars to paired and selected comparisons. Breaking changes are announced plainly and frequently, consistent with a package using 0.x to fix its shape before committing.
The patchwork removal is described as something that will eventually break dependent code, so the near-term work is likely completing that migration and settling the split_plot() parameters introduced alongside it. A 1.0 would signal the breaking-change cadence is ending, and nothing here indicates that yet.
Other Infra & APIs 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 neonUtilities or tidyplots.
Recurrent-event modelling settles, with mean_no() promoted to stable.
Nonparametric change point detection swaps p-values for importance scores.
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
Wavelet trend estimation tightens the defaults it shipped with.
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
mice can finally predict, not just estimate, from multiply imputed data.
See all neonUtilities alternatives → · See all tidyplots alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. neonUtilities and tidyplots 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. neonUtilities and tidyplots 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 Infra & APIs products to evaluate alongside.
Top neonUtilities alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "neonUtilities alternatives" section above for the current picks, or visit /alternatives/neonutilities for the full list with editorial commentary on each.
Top tidyplots alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tidyplots alternatives" section above for the current picks, or visit /alternatives/tidyplots for the full list with editorial commentary on each.