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bagyo vs spEDM

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

bagyo vs spEDM: at a glance

FeaturebagyospEDM
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesopen data, tropical cyclones, philippines, data packagecausal-inference, spatial-analysis, empirical-dynamic-modeling, r-package
Last editorial update57m ago2h ago
WebsiteVisit →Visit →

What is bagyo?

bagyo reached CRAN as a Philippine tropical cyclone dataset, with its tags stamped out of order.

A data package distributing Philippine Area of Responsibility tropical cyclone records, developed through 2024 pre-releases and accepted by CRAN in early 2026. The substantive release is v0.2.0: 2021 and 2022 typhoon data added, an unexported helper for downloading cyclone reports, CITATION.cff, an R 4.1 dependency for the base pipe, and a full pass over vignettes, tests and README. The v0.1.1 tag announcing the first CRAN release carries no content and is stamped two hours after v0.2.0.

Read the full bagyo trajectory →

What is spEDM?

Spatial causal discovery in R, one exposed method per release

spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.

Read the full spEDM trajectory →

bagyo vs spEDM: editorial side-by-side

B
bagyo
ANALYTICS
0.0

bagyo reached CRAN as a Philippine tropical cyclone dataset, with its tags stamped out of order.

◆ Current state

A data package distributing Philippine Area of Responsibility tropical cyclone records, developed through 2024 pre-releases and accepted by CRAN in early 2026. The substantive release is v0.2.0: 2021 and 2022 typhoon data added, an unexported helper for downloading cyclone reports, CITATION.cff, an R 4.1 dependency for the base pipe, and a full pass over vignettes, tests and README. The v0.1.1 tag announcing the first CRAN release carries no content and is stamped two hours after v0.2.0.

◆ Where it's heading

The package is establishing itself as a citable, yearly-updated dataset rather than a one-off scrape — the download helper and the '2022 data and general yearly upkeep' commit both point at a recurring refresh, and the CRAN DOI and CITATION file exist so the data can be cited in papers. It sits alongside the same maintainer's other public-health and survey data packages, which received matching repository upkeep in the same month.

◆ Prediction

Expect an annual data release adding the next typhoon season, since that is the only recurring change in the history and the download helper was written to support it.

S
spEDM
ANALYTICS
0.0

Spatial causal discovery in R, one exposed method per release

◆ Current state

spEDM brings empirical dynamic modeling to spatial data — cross mapping, convergent cross mapping and pattern causality over spatial vector and raster inputs, with the numerics in C++ behind S4 generics. The recent releases have exposed geographical pattern causality and spatially convergent partial cross mapping at the R level with vignettes, and 1.12 turns to consolidating the API. It is part of the stscl family alongside the temporal-domain tEDM, with which it shares both its C++ core and its maintainer.

◆ Where it's heading

The cadence is steady and predictable: each release surfaces one more EDM method as an R-level API with a vignette, then spends the rest of its notes on parameter-handling consistency across the generics. Breaking changes are frequent and deliberate — argument renames, parameter reordering, NA-handling defaults — which reads as a package still settling its interface while the method surface expands. Shared changes appear in tEDM within days, so interface churn lands on both packages at once.

◆ Prediction

Expect the next release to expose another causality variant at the R level with an accompanying vignette, and to continue renaming or reordering parameters toward consistency across the spatial and temporal packages.

Alternatives to bagyo and spEDM

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 bagyo or spEDM.

See all bagyo alternatives → · See all spEDM alternatives →

Recent activity from bagyo and spEDM

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

  1. 4mo agospEDMData slicing for large-scale pattern causality, plus API breaks
  2. 6mo agospEDMspEDM 1.11
  3. 6mo agospEDMSpatially convergent partial cross mapping reaches the R API
  4. 7mo agobagyobagyo v0.1.1
  5. 7mo agobagyo2021 and 2022 typhoon data added
  6. 8mo agospEDMRaster cross mapping with anisotropic embedding
  7. 11mo agospEDMConfigurable distance metrics and multithreaded distance computation
  8. 1y agospEDMSpatial logistic map exposed at the R level
  9. 2y agobagyoPre-release for Zenodo archiving
  10. 2y agobagyoInitial pre-release

Frequently asked questions

What is the difference between bagyo and spEDM?

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

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

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

What are the best alternatives to spEDM?

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