rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
distributional vs OHPL
A side-by-side editorial comparison of distributional and OHPL — release velocity, themes, recent moves, and the top alternatives to consider.
distributional vs OHPL: at a glance
What is distributional?
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
What is OHPL?
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.
distributional vs OHPL: editorial side-by-side
distributional
0.0ANALYTICS
distributional taught + and - to work on any pair of distributions, closing the algebra it started with.
◆ Current state
The R package providing vectorised distribution objects — the substrate that forecasting and anomaly tooling in the same ecosystem builds on. Cadence has picked up sharply, with four releases in the six months to June 2026 against roughly one a year before that. Two kinds of work alternate: adding distribution families (Dirichlet, Horseshoe, Laplace, multivariate t, g-and-k, the extreme-value pair) and deepening what can be computed generically across all of them.
◆ Where it's heading
The generic-computation thread is the one that matters and it has been building steadily: a Monte Carlo default method for cdf(), has_symmetry() to let algorithms specialise, hdr() moving to exact results for symmetric distributions and 4096 quantiles elsewhere, open-versus-closed support intervals. Version 0.8.0 is where that thread arrives somewhere — arithmetic on arbitrary distributions, with closed forms used when they exist and numerical convolution when they do not. The package is positioning itself as a computational layer rather than a catalogue, which is consistent with how weird and the forecasting packages consume it.
◆ Prediction
Expect the numerical machinery behind dist_convolved() to be reused for other operators, and more generics like has_symmetry() that let downstream algorithms take exact paths when a distribution supports them.
OHPL
0.0ANALYTICS
A 2017 chemometrics method frozen in place, visited only when CRAN changes its documentation rules.
◆ Current state
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.
◆ Where it's heading
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.
◆ Prediction
Expect the next release whenever CRAN introduces another documentation or packaging check; there is no indication the method itself will be extended.
Alternatives to distributional and OHPL
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 distributional or OHPL.
audubon
audubon's release feed is almost entirely Renovate bumping the JavaScript toolchain behind its Japanese text splitter.
affiner
affiner is quietly turning a grid transformation helper into a small computational geometry library.
ageproR
ageproR spent two years chasing a moving file format, then added the recruitment models that justify the effort.
r-ledger
ledger adds a Rust toolchain fallback, so beancount imports work whether or not the Python tooling is installed.
gridpattern
gridpattern keeps widening its catalogue, and the newest patterns finally use the device's own line rendering.
See all distributional alternatives → · See all OHPL alternatives →
Recent activity from distributional and OHPL
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
- 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators ⚡
- 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
- 4mo agoOHPLRd documentation HTML validation fixed
- 5mo agodistributionalDirichlet and Horseshoe distributions added
- 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
- 2y agoOHPLDocumentation modernized to current R conventions
- 7y agoOHPLCode restyled and repository links updated
- 9y agoOHPLCitation information and documentation site updated
- 9y agoOHPLPrediction and evaluation functions complete the package
Frequently asked questions
What is the difference between distributional and OHPL?
They serve adjacent needs but don't currently overlap on shipped themes. distributional and OHPL 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.
Is distributional better than OHPL?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and OHPL 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 Analytics products to evaluate alongside.
What are the best alternatives to distributional?
Top distributional alternatives in Analytics are ranked by recent ship velocity. Browse the "distributional alternatives" section above for the current picks, or visit /alternatives/distributional-r for the full list with editorial commentary on each.
What are the best alternatives to OHPL?
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.