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distributional vs n1qn1c

A side-by-side editorial comparison of distributional and n1qn1c — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

distributional vs n1qn1c: at a glance

Featuredistributionaln1qn1c
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, probability-distributions, distribution-arithmetic, numerical-methodsnumerical-optimization, quasi-newton, thread-safety, memory-safety
Last editorial update47m 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 n1qn1c?

A Fortran-descended optimizer got thread-safe, then found two flags that never worked.

n1qn1c is a quasi-Newton optimization routine translated from Fortran to C, used as a solver backend by the nlmixr2 modeling stack rather than called directly by most users. Its two 2026 releases are a concentrated safety pass: global state converted to thread_local, static removed from local variables in the translated code, integer overflow guards added, and memory leaks closed in the R callback wrappers — plus the gcc-asan and valgrind fixes CRAN asked for.

Read the full n1qn1c trajectory →

distributional vs n1qn1c: 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.

N
n1qn1c
ANALYTICS
0.0

A Fortran-descended optimizer got thread-safe, then found two flags that never worked.

◆ Current state

n1qn1c is a quasi-Newton optimization routine translated from Fortran to C, used as a solver backend by the nlmixr2 modeling stack rather than called directly by most users. Its two 2026 releases are a concentrated safety pass: global state converted to thread_local, static removed from local variables in the translated code, integer overflow guards added, and memory leaks closed in the R callback wrappers — plus the gcc-asan and valgrind fixes CRAN asked for.

◆ Where it's heading

The package is being hardened for use inside a parallel modeling framework rather than extended. The audit that produced the thread-safety work also surfaced two plain bugs in features users would have assumed worked: restart = TRUE left the mode at 2 instead of 3 because of a typo, and assign = TRUE referenced the wrong field name so the compressed Hessian was never written to the supplied environment. Earlier work points the same direction — the 6.0.1-12 function-pointer interface exists so nlmixr2est does not need resubmission when this package changes.

◆ Prediction

Expect further memory-safety and sanitizer work rather than algorithmic change; a Fortran-translated numerical core under CRAN's checking regime generates that kind of release indefinitely.

Alternatives to distributional and n1qn1c

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 n1qn1c.

See all distributional alternatives → · See all n1qn1c alternatives →

Recent activity from distributional and n1qn1c

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. 4mo agon1qn1crestart and assign flags fixed; sanitizer issues cleared
  5. 4mo agon1qn1cGlobal state converted to thread_local
  6. 5mo agodistributionalDirichlet and Horseshoe distributions added
  7. 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
  8. 1y agon1qn1cFunction-pointer API decouples nlmixr2est releases
  9. 1y agodistributionalMonte Carlo cdf() default method; g-and-k, g-and-h and extreme-value families

Frequently asked questions

What is the difference between distributional and n1qn1c?

Both compete on the same themes — r-package — within Analytics. distributional and n1qn1c 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 n1qn1c?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributional and n1qn1c 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 n1qn1c?

Top n1qn1c alternatives in Analytics are ranked by recent ship velocity. Browse the "n1qn1c alternatives" section above for the current picks, or visit /alternatives/n1qn1c for the full list with editorial commentary on each.