← Back to home
Comparison · Analytics

labelled vs spEDM

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

Shared themes:r-package

labelled vs spEDM: at a glance

FeaturelabelledspEDM
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themessurvey-data, data-labels, stata-spss, metadatacausal-inference, spatial-analysis, empirical-dynamic-modeling, r-package
Last editorial update42m ago2h ago
WebsiteVisit →Visit →

What is labelled?

The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.

labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.

Read the full labelled 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 →

labelled vs spEDM: editorial side-by-side

L
labelled
ANALYTICS
0.0

The bridge between Stata/SPSS labelled data and tidy R keeps widening, one integration at a time.

◆ Current state

labelled manages variable labels, value labels and user-defined missing values on data imported from Stata, SPSS and SAS, filling the gap between those formats' metadata and R's native types. Recent releases have pushed outward from the core label accessors: survey design objects from the survey package are now supported throughout, look_for() results can be rendered as formatted gt tables, and dictionary data frames convert in both directions. Error messaging moved wholesale to cli in 2.14.0.

◆ Where it's heading

The arc is toward being usable wherever labelled data ends up, not just where it is loaded. Each recent release either extends support to another object type — survey designs, packed columns, plain vectors, tibbles with list columns — or adds a conversion path between labels and some other representation. The look_for() search function has become a second centre of gravity alongside the label accessors, accumulating its own output formats and long-format conversions.

◆ Prediction

The pattern of adding compatibility with one more object type or output format per release is stable and likely continues. Nothing in these entries indicates a change to the underlying haven_labelled representation the package is built on.

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 labelled 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 labelled or spEDM.

See all labelled alternatives → · See all spEDM alternatives →

Recent activity from labelled 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. 8mo agospEDMRaster cross mapping with anisotropic embedding
  5. 9mo agolabelledFormatted look_for() tables and two-way dictionary conversion
  6. 11mo agolabelledSurvey design objects supported across the package
  7. 11mo agospEDMConfigurable distance metrics and multithreaded distance computation
  8. 1y agospEDMSpatial logistic map exposed at the R level
  9. 1y agolabelledRegression in set_variable_labels() corrected
  10. 1y agolabelledcli adopted for all messaging; null_action gains options
  11. 2y agolabelledCustom functions can rewrite variable and value labels in bulk
  12. 3y agolabelledPacked columns supported and label attributes exposed directly

Frequently asked questions

What is the difference between labelled and spEDM?

Both compete on the same themes — r-package — within Analytics. labelled 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 labelled better than spEDM?

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

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