rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
A side-by-side editorial comparison of distributions3 and OHPL — release velocity, themes, recent moves, and the top alternatives to consider.
distributions3 changes hands to Achim Zeileis, and a moment calculation bug goes with it.
An R package giving probability distributions a consistent object interface — d/p/q/r functions, moments, and prodist() methods that extract a fitted distribution from a regression object. Releases are infrequent, roughly one a year, and the last one is largely administrative: Achim Zeileis takes over maintenance from Alex Hayes, with all URLs and documentation updated to match, alongside a fix to incorrect moment calculations reported by a user.
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
OHPL implements ordered homogeneity pursuit lasso, a variable selection method for high-dimensional spectroscopic data that groups correlated predictors before applying a lasso. The functional package was complete by 1.2 in 2017, when prediction, performance evaluation and simulated data generation functions were added. Every release since has touched documentation and packaging only.
An R package giving probability distributions a consistent object interface — d/p/q/r functions, moments, and prodist() methods that extract a fitted distribution from a regression object. Releases are infrequent, roughly one a year, and the last one is largely administrative: Achim Zeileis takes over maintenance from Alex Hayes, with all URLs and documentation updated to match, alongside a fix to incorrect moment calculations reported by a user.
The package's growth has come in two modes. Early releases absorbed whole families of distributions from outside contributors — the extreme-value set, Erlang, later the Poisson binomial — while later ones tightened the interface itself with is_discrete() and is_continuous() generics and elementwise type-safety when applying a distribution vector to a numeric vector. The handover is the notable event in the current window: maintenance moves to the author of the surrounding statistical ecosystem this package already integrates with through prodist() and countreg, which suggests the interface work will continue over the distribution-collection work.
Expect closer alignment with Zeileis's own packages, with prodist() coverage widening to more model classes; the entries here do not indicate whether new distribution families remain on the agenda.
OHPL implements ordered homogeneity pursuit lasso, a variable selection method for high-dimensional spectroscopic data that groups correlated predictors before applying a lasso. The functional package was complete by 1.2 in 2017, when prediction, performance evaluation and simulated data generation functions were added. Every release since has touched documentation and packaging only.
This is a published-method package in the archival phase: the algorithm is fixed, the paper is cited, and the maintainer keeps it installable. The releases read as a timeline of R packaging conventions rather than of the method — tidyverse code style in 2019, roxygen2 Markdown and bibentry() in 2024, Rd HTML validation in 2026. Gaps of two to five years between releases are normal here.
Expect the next release whenever CRAN introduces another documentation or packaging check; there is no indication the method itself will be extended.
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 distributions3 or OHPL.
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
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See all distributions3 alternatives → · See all OHPL alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. distributions3 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. distributions3 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 distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.
Top OHPL alternatives in Analytics are ranked by recent ship velocity. Browse the "OHPL alternatives" section above for the current picks, or visit /alternatives/ohpl for the full list with editorial commentary on each.