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distributional vs lineup2

A side-by-side editorial comparison of distributional and lineup2 — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

distributional vs lineup2: at a glance

Featuredistributionallineup2
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodssample-mixups, distance-metrics, r-package, bioinformatics
Last editorial update48m ago1h 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 lineup2?

lineup2 ships once every few years, and 2026's release is a logo and a core-count tweak.

lineup2 provides distance-based tools for detecting sample mix-ups between related datasets — comparing rows and columns of two matrices to find swapped or mislabeled samples. Its visible history is four releases spread across six years, and the capability surface has barely moved since plot_sample() and the propdiff distance arrived in 0.4. Version 0.8 in July 2026 adds a package logo and redefines cores=0 to mean all-but-one core.

Read the full lineup2 trajectory →

distributional vs lineup2: 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.

L
lineup2
ANALYTICS
0.0

lineup2 ships once every few years, and 2026's release is a logo and a core-count tweak.

◆ Current state

lineup2 provides distance-based tools for detecting sample mix-ups between related datasets — comparing rows and columns of two matrices to find swapped or mislabeled samples. Its visible history is four releases spread across six years, and the capability surface has barely moved since plot_sample() and the propdiff distance arrived in 0.4. Version 0.8 in July 2026 adds a package logo and redefines cores=0 to mean all-but-one core.

◆ Where it's heading

This is a finished, single-purpose package in maintenance. The substantive changes across the whole window are plotting conveniences and one parallelism default; nothing in the entries points at new distance measures, new input formats, or expanded scope. The release cadence — five years between 0.6 and 0.8 — reads as a tool the author considers done.

◆ Prediction

Further releases are likely to stay small: a plotting option, a parallelism detail, or a check-farm fix. The entries give no signal of planned feature work.

Alternatives to distributional and lineup2

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 lineup2.

See all distributional alternatives → · See all lineup2 alternatives →

Recent activity from distributional and lineup2

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

  1. 1mo agolineup2cores=0 now leaves one core free
  2. 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
  3. 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators
  4. 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
  5. 5mo agodistributionalDirichlet and Horseshoe distributions added
  6. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  7. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families
  8. 5y agolineup2plot_sample() gains xlim and ylim control
  9. 5y agolineup2plot_sample() and the propdiff distance added
  10. 5y agolineup2Package description revised for CRAN resubmission

Frequently asked questions

What is the difference between distributional and lineup2?

Both compete on the same themes — r-package — within Analytics. distributional and lineup2 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 lineup2?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and lineup2 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 lineup2?

Top lineup2 alternatives in Analytics are ranked by recent ship velocity. Browse the "lineup2 alternatives" section above for the current picks, or visit /alternatives/lineup2-r for the full list with editorial commentary on each.