distributions3
distributions3 0.3.0 adds sample-based distributions and likelihood derivatives
Comparison · Analytics
distributional vs tulpa
A side-by-side editorial comparison of distributional and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.
distributional vs tulpa: at a glance
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.
What is tulpa?
The 0.0.x train stops at CRAN: tulpa's engine ships to the ecosystem it already anchors.
tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.
distributional vs tulpa: editorial side-by-side
distributional
0.0ANALYTICS
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.
tulpa
7.5ANALYTICS
The 0.0.x train stops at CRAN: tulpa's engine ships to the ecosystem it already anchors.
◆ Current state
tulpa is the C++/R Bayesian spatial inference engine sitting under gcol33's family of ecological occupancy packages, tagging 0.0.x releases several times a week. 0.1.0 is its first CRAN release, and the notes state outright that the engine surface is unchanged from 0.0.198 — the work is packaging discipline: local T bindings rebound to n_t/n_times, OpenMP teams capped under R CMD check, the pkgdown deploy narrowed, an aspell dictionary added. The window behind it splits between the S3 generics conversion and numerical-correctness work in the nested-Laplace grid.
◆ Where it's heading
Two moves in nine days point at the same destination: the generics conversion made tulpa extensible by downstream packages, and CRAN admission makes it installable by them. The current cadence — several tags a week, some existing only to record a measurement that produced no code change — does not survive CRAN's submission overhead, so the release rhythm has to slow whether or not the project intends it. The correctness work still clusters on the joint nested-Laplace driver, and 0.1.0 extends the same diagnostics habit with .NL_AXIS_SD_REASONS, a closed vocabulary for an outer axis whose grid does not contain its own posterior mode.
◆ Prediction
Expect tulpaObs to follow tulpa onto CRAN, since it is the consumer whose registrations the engine has spent this window unblocking, and expect the version line to move in larger, less frequent steps now that each one carries a submission.
Alternatives to distributional and tulpa
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 tulpa.
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
Fulcrum
Fulcrum is betting its whole map stack on Esri, with a hard Google Maps cutoff on September 1.
Holistics
Holistics keeps fencing in the AI layer it spent the summer building.
See all distributional alternatives → · See all tulpa alternatives →
Recent activity from distributional and tulpa
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 23h agotulpaFirst CRAN release: engine surface unchanged from 0.0.198 ⚡
- 4d agotulpatulpa_re_aghq() exposes the mode/theta cross-Hessian
- 8d agotulpaDense batched joint path could silently drop a grid cell
- 8d agotulpaCalibration and goodness-of-fit entry points become S3 generics ⚡
- 9d agotulpaCUDA backend had two definitions; link order decided if it ran
- 9d agotulpaHyperparameter bounds now flag when they leave the node range
- 1mo agodistributionalConditional S3 registration so the package loads on R before 4.3
- 1mo agodistributionalDistribution arithmetic: FFT convolution behind the + and - operators ⚡
- 2mo agodistributionalVectorised p in quantile() for inflated distributions; open brackets on infinite bounds
- 5mo agodistributionalDirichlet and Horseshoe distributions added
- 7mo agodistributionalhas_symmetry() generic, exact HDRs for symmetric distributions
- 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 tulpa?
They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 7.5 vs 0.0), with 2 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 distributional better than tulpa?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tulpa is currently shipping more aggressively (velocity 7.5 vs 0.0), with 2 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 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 tulpa?
Top tulpa alternatives in Analytics are ranked by recent ship velocity. Browse the "tulpa alternatives" section above for the current picks, or visit /alternatives/tulpa for the full list with editorial commentary on each.