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sdsfun vs skylight

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

Shared themes:r-package

sdsfun vs skylight: at a glance

Featuresdsfunskylight
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesspatial-statistics, geodetector, spatial-clustering, rcppastronomy, illuminance, cpp-port, scientific-computing
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is sdsfun?

A spatial-statistics utility package exists to be depended on, and is built accordingly.

sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.

Read the full sdsfun trajectory →

What is skylight?

A frozen astronomical model quietly became the inner loop of its sibling's optimizer.

skylight returns sun and moon illuminance, azimuth and altitude for a given date, time and location, implemented as a near-verbatim transcription of a 1987 US Naval Observatory circular. The model formulation has not changed since the initial 2022 release and the author states so explicitly. Everything shipped since has been packaging, citation and speed: v1.3 moved the main routine from R to C++, and v1.4 removed a parameter check that was flooding the console with messages.

Read the full skylight trajectory →

sdsfun vs skylight: editorial side-by-side

S
sdsfun
ANALYTICS
0.0

A spatial-statistics utility package exists to be depended on, and is built accordingly.

◆ Current state

sdsfun collects spatial data science utilities — neighbour lists, spatial constrained clustering, discretization, dummy variable generation, geographical detector statistics and projection helpers — with the computationally heavy parts implemented in Rcpp. It was assembled quickly across late 2024, adding a function set roughly every three weeks, and has slowed since to a couple of releases a year. The most recent work is corrective: no longer initializing the RNG state at load, fixing matrix inputs misread as vectors, and clearing an Armadillo deprecation.

◆ Where it's heading

This is infrastructure for a family of packages rather than an end-user tool, and the changelog says so directly — functions were added to support gdverse and sesp, and moran_test was migrated in from geocomplexity. That migration pattern is the defining move: capability consolidates here so the downstream packages can share it instead of each carrying its own copy. Growth has slowed as that consolidation completed, leaving correctness and dependency upkeep.

◆ Prediction

Given the package moves when its dependents need something, the next release most likely brings in another shared function or responds to a downstream requirement rather than following its own plan. Armadillo and CRAN check changes remain the reliable source of maintenance work.

S
skylight
ANALYTICS
0.0

A frozen astronomical model quietly became the inner loop of its sibling's optimizer.

◆ Current state

skylight returns sun and moon illuminance, azimuth and altitude for a given date, time and location, implemented as a near-verbatim transcription of a 1987 US Naval Observatory circular. The model formulation has not changed since the initial 2022 release and the author states so explicitly. Everything shipped since has been packaging, citation and speed: v1.3 moved the main routine from R to C++, and v1.4 removed a parameter check that was flooding the console with messages.

◆ Where it's heading

This is a reference implementation of a published algorithm rather than a product accumulating features, and it is being maintained that way. The movement that does occur is driven from downstream: the C++ port was written for the inverse-modelling loop in the sibling skytrackr package, which calls skylight repeatedly during optimization. That reframes skylight from a standalone calculator into the compute kernel another package's fitting routine depends on.

◆ Prediction

With the model formulation deliberately fixed and the C++ path already in place, the next release is most likely another small maintenance fix. The entries give no indication of planned new capability.

Alternatives to sdsfun and skylight

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 sdsfun or skylight.

See all sdsfun alternatives → · See all skylight alternatives →

Recent activity from sdsfun and skylight

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

  1. 9mo agoskylightNoisy parameter check removed from console output
  2. 10mo agoskylightCore routine moves from R to C++ for repeated-call speed
  3. 10mo agosdsfunPackage load stops touching the RNG state
  4. 1y agosdsfunUnified partial correlation testing and head/tails discretization
  5. 1y agosdsfunMissing-value handling added to linear trend removal
  6. 1y agosdsfunCovariate-based detrending and long-to-matrix spatial reshaping
  7. 1y agosdsfunSpatially constrained hierarchical clustering and SPADE estimation
  8. 1y agosdsfunFast geodetector q-value estimator added
  9. 2y agoskylightCitation updated after the companion paper published
  10. 3y agoskylightSkylight v1.1
  11. 3y agoskylightFirst release: sun and moon illuminance from the 1987 USNO circular

Frequently asked questions

What is the difference between sdsfun and skylight?

Both compete on the same themes — r-package — within Analytics. sdsfun and skylight 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 sdsfun better than skylight?

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

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

What are the best alternatives to skylight?

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