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distributional vs units

A side-by-side editorial comparison of distributional and units — release velocity, themes, recent moves, and the top alternatives to consider.

distributional vs units: at a glance

Featuredistributionalunits
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsmeasurement units, r package, udunits2, breaking parser change
Last editorial update49m ago4h ago
WebsiteVisit →Visit →

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.

Read the full distributional trajectory →

What is units?

units reaches 1.0 by rewriting its parser and accepting the breakage that comes with it.

units attaches physical units to R vectors on top of the udunits2 library, covering arithmetic, conversion and ggplot2 scales. Version 1.0-0 replaced the unit-expression tokenizer so numbers are consistently treated as prefixes, and expressions like ml/min/1.73m^2 now parse the way physiologists write them. Printing follows NIST conventions, and 1.0-1 is a fix pass over memory handling and parser edge cases.

Read the full units trajectory →

distributional vs units: editorial side-by-side

D0.0

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.

U
units
ANALYTICS
0.0

units reaches 1.0 by rewriting its parser and accepting the breakage that comes with it.

◆ Current state

units attaches physical units to R vectors on top of the udunits2 library, covering arithmetic, conversion and ggplot2 scales. Version 1.0-0 replaced the unit-expression tokenizer so numbers are consistently treated as prefixes, and expressions like ml/min/1.73m^2 now parse the way physiologists write them. Printing follows NIST conventions, and 1.0-1 is a fix pass over memory handling and parser edge cases.

◆ Where it's heading

The long 0.8-x run was accretion — ggplot2 scales absorbed from ggforce, ud_convert(), matrix methods, steady performance work — while known parsing defects stayed in place. The 1.0 release finally traded backwards compatibility for correct parsing, and 1.0-1's pointer-wrapping and exception-propagation work suggests the C++ glue is being hardened behind it. Fixes cluster at the udunits2 boundary, which remains the main source of surprises.

◆ Prediction

Expect continued patch releases against udunits2 quirks and the new tokenizer's fallout rather than new surface area, with the ggplot2 integration and conversion helpers already in place.

Alternatives to distributional and units

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 units.

See all distributional alternatives → · See all units alternatives →

Recent activity from distributional and units

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
  2. 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators
  3. 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
  4. 5mo agodistributionalDirichlet and Horseshoe distributions added
  5. 5mo agounitsMemory-leak and udunits2 parsing fixes after 1.0
  6. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  7. 10mo agounitsNew tokenizer parses compound units correctly
  8. 1y agounitsCopy semantics fix in ud_convert(); C++17 for old R
  9. 1y agounitscbind/rbind methods, ud_convert() and broad fixes
  10. 1y agounitsSilences a CRAN compiler warning
  11. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
  12. 2y agounitsRestores simplify=FALSE for identical units

Frequently asked questions

What is the difference between distributional and units?

They serve adjacent needs but don't currently overlap on shipped themes. distributional and units 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 units?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and units 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 units?

Top units alternatives in Analytics are ranked by recent ship velocity. Browse the "units alternatives" section above for the current picks, or visit /alternatives/units-r for the full list with editorial commentary on each.