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mlr3fda vs tulpa

A side-by-side editorial comparison of mlr3fda and tulpa — release velocity, themes, recent moves, and the top alternatives to consider.

mlr3fda vs tulpa: at a glance

Featuremlr3fdatulpa
SectorAnalyticsAnalytics
Velocity score2.57.5
Sparks · 30d02
Top themesmlr3, functional-data, feature-extraction, pipelinesbayesian-inference, cran-release, r-packages, spatial-modeling
Last editorial update5d ago7h ago
WebsiteVisit →Visit →

What is mlr3fda?

A functional-data feature factory for mlr3, shipping a new extractor almost every month.

mlr3fda adds functional data support to mlr3 pipelines through PipeOps that turn functional columns into tabular features. Five releases since March 2026 have taken it from Fourier features to a catalogue covering wavelets, derivatives, depth, registration, integration and the catch22 time-series feature set. Development tracks the {tf} package closely.

Read the full mlr3fda trajectory →

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.

Read the full tulpa trajectory →

mlr3fda vs tulpa: editorial side-by-side

M
mlr3fda
ANALYTICS
2.5

A functional-data feature factory for mlr3, shipping a new extractor almost every month.

◆ Current state

mlr3fda adds functional data support to mlr3 pipelines through PipeOps that turn functional columns into tabular features. Five releases since March 2026 have taken it from Fourier features to a catalogue covering wavelets, derivatives, depth, registration, integration and the catch22 time-series feature set. Development tracks the {tf} package closely.

◆ Where it's heading

The pattern is one or two new PipeOps per release with fixes to the previous batch alongside — Fourier in 0.4.0, registration in 0.5.0, depth and derivatives in 0.6.0, catch22 and integration in 0.7.0. Performance and parallel-safety work is folded in as it becomes necessary rather than deferred, as with the Fourier speedup and the mlr_reflections registration fix.

◆ Prediction

The extraction catalogue is filling out along established functional-data methods, so further tf-backed PipeOps are the likely next additions; the {tf} 0.5.0 compatibility release suggests upstream churn will keep setting the pace.

T
tulpa
ANALYTICS
7.5

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 mlr3fda 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 mlr3fda or tulpa.

See all mlr3fda alternatives → · See all tulpa alternatives →

Recent activity from mlr3fda and tulpa

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

  1. 17h agotulpaFirst CRAN release: engine surface unchanged from 0.0.198
  2. 4d agotulpatulpa_re_aghq() exposes the mode/theta cross-Hessian
  3. 8d agotulpaDense batched joint path could silently drop a grid cell
  4. 8d agotulpaCalibration and goodness-of-fit entry points become S3 generics
  5. 9d agotulpaCUDA backend had two definitions; link order decided if it ran
  6. 9d agotulpaHyperparameter bounds now flag when they leave the node range
  7. 1mo agomlr3fdaCompatibility with tf 0.5.0
  8. 1mo agomlr3fdacatch22 and integral feature extractors added
  9. 2mo agomlr3fdaDepth and derivative extractors; Fourier made several times faster
  10. 3mo agomlr3fdaCurve registration via PipeOpFDARegister
  11. 4mo agomlr3fdaFourier feature extraction and package-specific condition classes

Frequently asked questions

What is the difference between mlr3fda and tulpa?

They serve adjacent needs but don't currently overlap on shipped themes. tulpa is currently shipping more aggressively (velocity 7.5 vs 2.5), 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 mlr3fda 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 2.5), 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 mlr3fda?

Top mlr3fda alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3fda alternatives" section above for the current picks, or visit /alternatives/mlr3fda 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.