rjd3highfreq
rjd3highfreq ships whatever the Java side ships, and only occasionally says what that was.
Comparison · Analytics
distributional vs fairmodels
A side-by-side editorial comparison of distributional and fairmodels — release velocity, themes, recent moves, and the top alternatives to consider.
distributional vs fairmodels: 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 fairmodels?
fairmodels sits dormant for three years, resurfacing only to satisfy a CRAN check.
fairmodels audits classification models for bias, built around fairness_check() and parity-loss metrics on top of DALEX explainers. The last substantive work dates from 2021; the 2025 release is a single-line change swapping ifelse for if/else in fairness_heatmap. Version 0.2.2 set the package's core design when it superseded metric differences with ratios.
distributional vs fairmodels: 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.
fairmodels
0.0ANALYTICS
fairmodels sits dormant for three years, resurfacing only to satisfy a CRAN check.
◆ Current state
fairmodels audits classification models for bias, built around fairness_check() and parity-loss metrics on top of DALEX explainers. The last substantive work dates from 2021; the 2025 release is a single-line change swapping ifelse for if/else in fairness_heatmap. Version 0.2.2 set the package's core design when it superseded metric differences with ratios.
◆ Where it's heading
The release history describes a package that reached its intended shape early and has been custodial since — the gap from August 2022 to October 2025 carries no functional change at all. What movement exists is CRAN-driven: documentation compliance, example runtimes, coding-style notes. The fairness metrics themselves have not changed since the parity_loss corrections of 2020.
◆ Prediction
On this cadence the next release is most likely another CRAN-prompted one-liner rather than new fairness metrics; nothing in these entries points to active development.
Alternatives to distributional and fairmodels
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 fairmodels.
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 fairmodels alternatives →
Recent activity from distributional and fairmodels
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
- 5mo agodistributionalDirichlet and Horseshoe distributions added
- 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
- 9mo agofairmodelsOne-line fix in fairness_heatmap
- 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
- 3y agofairmodelsCRAN compliance fixes and citation update
- 4y agofairmodelsCRAN v1.2.0
- 5y agofairmodelsCRAN v1.1.0
- 5y agofairmodelsDocumentation fixes and trimmed example runtimes
- 5y agofairmodelsCorrects parity_loss in the cutoff functions
Frequently asked questions
What is the difference between distributional and fairmodels?
They serve adjacent needs but don't currently overlap on shipped themes. distributional and fairmodels 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 fairmodels?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and fairmodels 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 fairmodels?
Top fairmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "fairmodels alternatives" section above for the current picks, or visit /alternatives/fairmodels for the full list with editorial commentary on each.