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Comparison · Analytics
distributions3 vs nat.templatebrains
A side-by-side editorial comparison of distributions3 and nat.templatebrains — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
distributions3 vs nat.templatebrains: at a glance
| Feature | distributions3 | nat.templatebrains |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 6.3 | 0.0 |
| Sparks · 30d | 1 | 0 |
| Top themes | r-package, probability-distributions, empirical-distributions, likelihood-inference | neuroscience, image-registration, natverse, template-brains |
| Last editorial update | 9h ago | 2d ago |
| Website | Visit → | 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.
What is nat.templatebrains?
The natverse package that taught neuron data to remember which brain space it lives in.
nat.templatebrains handles registration between template brain spaces — xform_brain(), mirror_brain(), and the bridging-registration graph that finds a path from one template to another. Since 0.8 transformed objects carry a regtemplate attribute recording their space, so downstream natverse functions can usually infer it rather than being told. The package is now in low-cadence maintenance, with 1.2.1 blocked on a CRAN submission window rather than on code.
distributions3 vs nat.templatebrains: editorial side-by-side
distributions3
6.3ANALYTICS
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.
nat.templatebrains
0.0ANALYTICS
The natverse package that taught neuron data to remember which brain space it lives in.
◆ Current state
nat.templatebrains handles registration between template brain spaces — xform_brain(), mirror_brain(), and the bridging-registration graph that finds a path from one template to another. Since 0.8 transformed objects carry a regtemplate attribute recording their space, so downstream natverse functions can usually infer it rather than being told. The package is now in low-cadence maintenance, with 1.2.1 blocked on a CRAN submission window rather than on code.
◆ Where it's heading
The substantive design work finished years ago. The arc ran from manual space bookkeeping, through memoised bridging-sequence lookup, to self-describing objects at 0.8 — after which releases became dependency hygiene and CRAN paperwork. Two of the last three entries change no code at all: one demotes Morpho from Imports to Suggests, the other updates submission comments.
◆ Prediction
The next release is most likely the delayed 1.2.1 CRAN submission itself. The entries show no pending functional work beyond it.
Alternatives to distributions3 and nat.templatebrains
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 nat.templatebrains.
Lightdash
Lightdash keeps handing authoring to outside agents and keeping the governed layer for itself.
Delta Lake
A 4.4.0 tag appears, but the feed carries only its release plumbing
Basedash
Basedash keeps pushing its data out of the workspace — now to people without accounts
RStudio
RStudio ships through release branches, and the notes are commit messages
dbt Core
dbt Fusion's second beta is adapter work: ClickHouse gets materializations, indexes, and catalogs
See all distributions3 alternatives → · See all nat.templatebrains alternatives →
Recent activity from distributions3 and nat.templatebrains
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 9h agodistributions3Empirical distributions, plus score and hessian generics ⚡
- 29d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
- 10mo agodistributions3ggplot2 compatibility for the plotting functions
- 1y agonat.templatebrainsv1.2.1: update cran-comments for submission
- 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
- 3y agonat.templatebrainsMorpho demoted from Imports to Suggests
- 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
- 4y agodistributions3Extreme-value family, Erlang, and a plotting generic
- 8y agonat.templatebrainsIdentity transforms skipped, constructor requirements relaxed
- 9y agonat.templatebrainsTransformed objects now carry their registration space ⚡
Frequently asked questions
What is the difference between distributions3 and nat.templatebrains?
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 nat.templatebrains?
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 nat.templatebrains?
Top nat.templatebrains alternatives in Analytics are ranked by recent ship velocity. Browse the "nat.templatebrains alternatives" section above for the current picks, or visit /alternatives/nat-templatebrains for the full list with editorial commentary on each.