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tidytransit vs weird

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

tidytransit vs weird: at a glance

Featuretidytransitweird
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr, transit, gtfs, routinganomaly-detection, r-package, distributional, robust-statistics
Last editorial update1h ago3h ago
WebsiteVisit →Visit →

What is tidytransit?

tidytransit tracks the GTFS spec as it grows, one reader and one router feature at a time.

tidytransit reads GTFS transit feeds into tidy data frames and computes travel times using a RAPTOR implementation. Recent work splits between the reader keeping pace with the spec — locations.geojson in 1.7.0, empty strings parsed as NA in 1.8.0 — and the router gaining realism, most recently in-seat transfers. Feed specifications are now pulled from the automatically parsed GTFS reference rather than maintained by hand.

Read the full tidytransit trajectory →

What is weird?

weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.

An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.

Read the full weird trajectory →

tidytransit vs weird: editorial side-by-side

T
tidytransit
ANALYTICS
0.0

tidytransit tracks the GTFS spec as it grows, one reader and one router feature at a time.

◆ Current state

tidytransit reads GTFS transit feeds into tidy data frames and computes travel times using a RAPTOR implementation. Recent work splits between the reader keeping pace with the spec — locations.geojson in 1.7.0, empty strings parsed as NA in 1.8.0 — and the router gaining realism, most recently in-seat transfers. Feed specifications are now pulled from the automatically parsed GTFS reference rather than maintained by hand.

◆ Where it's heading

The package has settled into tracking an external standard, which is why the changelog reads as a sequence of spec conformance items rather than a roadmap. Parsing responsibility keeps shifting outward to gtfsio, and data sources have moved with the ecosystem, from the retired transitfeeds API to MobilityData. Router changes are rarer than reader changes but land in the same releases.

◆ Prediction

Further GTFS spec features are the safest expectation, with GTFS-Flex the likeliest area now that locations.geojson reading is in place.

W
weird
ANALYTICS
0.0

weird rebuilt itself on distributional objects, and now the anomaly tooling composes with everything else.

◆ Current state

An R package for anomaly detection and unusual-observation diagnostics, at four releases with a long gap between the 2024 patch and the 2026 major line. The current shape is set by 2.0.0, which refactored the package onto distributional objects and renamed its central concept from density_scores() to surprisals(). Since then the work has been filling that structure in: surprisals for more model classes, faster bandwidth and probability calculations, and new visual diagnostics.

◆ Where it's heading

The refactor onto a shared distribution representation is the decision everything else follows from. It let 2.1.0 add hdr() and parameters() methods for kde objects rather than bespoke accessors, and it let 3.0.0 bring in dist_mclust() to turn a Gaussian mixture model into the same object type — so a mixture, a kernel density estimate and a fitted distribution all flow through one interface. The 3.0.0 additions lean visual and multivariate: outlier maps plotting score distance against orthogonal distance, biplot projections with variable axes overlaid, and an augment() method for robust PCA objects. Dependencies have been shed steadily along the way — lookout, interpolation — while mvscale() moved out and then back in.

◆ Prediction

Expect surprisals() coverage to keep extending to further model classes, and the multivariate and robust-PCA diagnostics introduced in 3.0.0 to gain the same distributional-object treatment as the univariate side.

Alternatives to tidytransit and weird

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 tidytransit or weird.

See all tidytransit alternatives → · See all weird alternatives →

Recent activity from tidytransit and weird

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

  1. 1mo agoweirdOutlier maps, biplot projections, and Gaussian mixtures as distributional objects
  2. 3mo agoweirdsurprisals() reaches glm objects; lookout dependency dropped
  3. 6mo agoweirdPackage refactored onto distributional objects; density_scores becomes surprisals
  4. 6mo agotidytransitIn-seat transfers supported in raptor() and travel_times()
  5. 11mo agotidytransitstop_group_distances() no longer ignores the by parameter
  6. 1y agotidytransitlocations.geojson reading; specs parsed from the GTFS reference
  7. 2y agoweirdWine reviews dataset replaced with a fetch function
  8. 2y agotidytransitfare_media_id added to fare_products spec
  9. 3y agotidytransitinterpolate_stop_times() added; router updated
  10. 3y agotidytransitDuplicated primary key check improved

Frequently asked questions

What is the difference between tidytransit and weird?

They serve adjacent needs but don't currently overlap on shipped themes. tidytransit and weird 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 tidytransit better than weird?

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

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

What are the best alternatives to weird?

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