incase
A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
Comparison · Analytics
cTMed vs sdsfun
A side-by-side editorial comparison of cTMed and sdsfun — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
cTMed vs sdsfun: at a glance
What is cTMed?
Continuous-time mediation effects get standardized centrality, six years into steady patch work
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
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.
cTMed vs sdsfun: editorial side-by-side
cTMed
2.5ANALYTICS
Continuous-time mediation effects get standardized centrality, six years into steady patch work
◆ Current state
cTMed computes direct, indirect and total effects for continuous-time mediation models, with delta-method, Monte Carlo and bootstrap variants of each. Development is a long run of patch releases from the jeksterslab account, roughly every two months, each adding a function or two. The latest adds standardized centrality measures and allows a diagonal sigma across ten standardized estimators.
◆ Where it's heading
The package is filling out a matrix rather than changing shape: for each effect type there is a delta-method, a Monte Carlo and a bootstrap path, and each release closes another cell. The 2025 releases were largely externally forced — an Armadillo 15.0.x transition at CRAN, a citation addition after the Psychological Methods paper landed — which suggests the statistical core has been settled since the 1.0.6 standardization revision.
◆ Prediction
The diagonal-sigma option has now reached the standardized estimators; extending it to the remaining unstandardized variants is the obvious next cell to fill.
sdsfun
0.0ANALYTICS
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.
Alternatives to cTMed and sdsfun
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 cTMed or sdsfun.
reda
A mature recurrent-event toolkit in careful maintenance, shedding weight rather than adding surface.
TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
cfbfastr
College football's open data client hit v2 — and now reports how many API calls you have left.
rnpn
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
See all cTMed alternatives → · See all sdsfun alternatives →
Recent activity from cTMed and sdsfun
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 27d agocTMedStandardized centrality measures and diagonal-sigma support
- 6mo agocTMedMinor method edits
- 10mo agocTMedPackage citation added for the Psychological Methods paper
- 10mo agocTMedArmadillo 15.0.x compatibility for CRAN
- 10mo agosdsfunPackage load stops touching the RNG state
- 1y agosdsfunUnified partial correlation testing and head/tails discretization
- 1y agocTMedStandardization reworked around the steady-state covariance matrix
- 1y agocTMedBootstrap centrality estimators and MCPhiSigma()
- 1y agosdsfunMissing-value handling added to linear trend removal
- 1y agosdsfunCovariate-based detrending and long-to-matrix spatial reshaping
- 1y agosdsfunSpatially constrained hierarchical clustering and SPADE estimation
- 1y agosdsfunFast geodetector q-value estimator added
Frequently asked questions
What is the difference between cTMed and sdsfun?
Both compete on the same themes — r-package — within Analytics. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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 cTMed better than sdsfun?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cTMed is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 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 cTMed?
Top cTMed alternatives in Analytics are ranked by recent ship velocity. Browse the "cTMed alternatives" section above for the current picks, or visit /alternatives/ctmed for the full list with editorial commentary on each.
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.