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Comparison · Analytics

distributions3 vs NWCTrends

A side-by-side editorial comparison of distributions3 and NWCTrends — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

distributions3 vs NWCTrends: at a glance

Featuredistributions3NWCTrends
SectorAnalyticsAnalytics
Velocity score6.30.0
Sparks · 30d10
Top themesr-package, probability-distributions, empirical-distributions, likelihood-inferencefisheries, state-space-models, reproducible-reporting, r-package
Last editorial update3h ago4d ago
WebsiteVisit →Visit →

What is distributions3?

distributions3 0.3.0 adds sample-based distributions and likelihood derivatives

An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.

Read the full distributions3 trajectory →

What is NWCTrends?

The salmon status-review trend package, maintained one federal review cycle at a time

NWCTrends fits multivariate state-space trend models to Pacific salmon population data and generates the tables and figures used in NOAA Northwest Fisheries Science Center viability and status reviews. Its release history maps onto those review cycles rather than a development calendar: v1.0 carries the 2015 review code, v1.25 the 2020 review, v1.30 the changes since. The 2026 v1.31 is internal restructuring and a dependency swap.

Read the full NWCTrends trajectory →

distributions3 vs NWCTrends: editorial side-by-side

D6.3

distributions3 0.3.0 adds sample-based distributions and likelihood derivatives

◆ Current state

An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.

◆ Where it's heading

Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().

◆ Prediction

With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.

N
NWCTrends
ANALYTICS
0.0

The salmon status-review trend package, maintained one federal review cycle at a time

◆ Current state

NWCTrends fits multivariate state-space trend models to Pacific salmon population data and generates the tables and figures used in NOAA Northwest Fisheries Science Center viability and status reviews. Its release history maps onto those review cycles rather than a development calendar: v1.0 carries the 2015 review code, v1.25 the 2020 review, v1.30 the changes since. The 2026 v1.31 is internal restructuring and a dependency swap.

◆ Where it's heading

Development is driven by reproducibility of a specific government reporting product, so most work goes into making the report generation configurable and the fitting assumptions explicit rather than into new modelling. The 2020 cycle removed hard-coded per-population hacks and made the fitting window an explicit argument; the 2023 cycle moved plot styling into package options and clarified how missing data and zeros are handled in the published tables.

◆ Prediction

The cadence suggests the next substantive release arrives with the next status review rather than before it, most likely continuing the move of report parameters out of function signatures and into structured configuration.

Alternatives to distributions3 and NWCTrends

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 distributions3 or NWCTrends.

See all distributions3 alternatives → · See all NWCTrends alternatives →

Recent activity from distributions3 and NWCTrends

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

  1. 4h agodistributions3Empirical distributions, plus score and hessian generics
  2. 29d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
  3. 7mo agoNWCTrendsReport params extracted to a list; gdata replaced with readxl
  4. 10mo agodistributions3ggplot2 compatibility for the plotting functions
  5. 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
  6. 3y agoNWCTrendsPlot options move into package globals; figure data exported to CSV
  7. 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
  8. 4y agodistributions3Extreme-value family, Erlang, and a plotting generic
  9. 5y agoNWCTrendsExplicit fitting window replaces implicit full-data fits
  10. 5y agoNWCTrendsInitial release packaging the 2015 status review code

Frequently asked questions

What is the difference between distributions3 and NWCTrends?

Both compete on the same themes — r-package — within Analytics. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is distributions3 better than NWCTrends?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to distributions3?

Top distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.

What are the best alternatives to NWCTrends?

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