← Back to home
Comparison · Analytics

cTMed vs haze

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

Shared themes:r-package

cTMed vs haze: at a glance

FeaturecTMedhaze
SectorAnalyticsAnalytics
Velocity score2.52.5
Sparks · 30d00
Top themesmediation-analysis, continuous-time-models, r-package, statistical-methodsneuroimaging, mesh-processing, interpolation, r-package
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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.

Read the full cTMed trajectory →

What is haze?

Four dormant years end with a modernization pass and an off-by-one fix in the C++ core

haze does nearest-neighbour smoothing and k-d tree interpolation on brain surface meshes. It sat untouched from April 2022 until July 2026, when a single release modernized it for current R versions and corrected an off-by-one error in the C++ code. It is not on CRAN and never will be — the package exceeds 50MB against CRAN's 5MB ceiling, a constraint its own initial release notes acknowledge.

Read the full haze trajectory →

cTMed vs haze: editorial side-by-side

C
cTMed
ANALYTICS
2.5

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.

H
haze
ANALYTICS
2.5

Four dormant years end with a modernization pass and an off-by-one fix in the C++ core

◆ Current state

haze does nearest-neighbour smoothing and k-d tree interpolation on brain surface meshes. It sat untouched from April 2022 until July 2026, when a single release modernized it for current R versions and corrected an off-by-one error in the C++ code. It is not on CRAN and never will be — the package exceeds 50MB against CRAN's 5MB ceiling, a constraint its own initial release notes acknowledge.

◆ Where it's heading

The July 2026 release arrived 56 minutes after its sibling regfusionr 0.3.0 from the same maintainer, which is the tell: this is a maintainer sweeping a set of related neuroimaging packages back into working order, not independent development on haze itself. haze is the dependency, regfusionr the consumer, and the substantive work sits on the regfusionr side. The off-by-one correction is the only change here that alters results.

◆ Prediction

Expect haze to move only when a downstream dfsp-spirit package needs it to — its cadence is driven by the sibling packages, not by its own roadmap.

Alternatives to cTMed and haze

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 haze.

See all cTMed alternatives → · See all haze alternatives →

Recent activity from cTMed and haze

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

  1. 18d agohazeVersion 0.3.0 -- Fixes and modernization
  2. 27d agocTMedStandardized centrality measures and diagonal-sigma support
  3. 6mo agocTMedMinor method edits
  4. 10mo agocTMedPackage citation added for the Psychological Methods paper
  5. 10mo agocTMedArmadillo 15.0.x compatibility for CRAN
  6. 1y agocTMedStandardization reworked around the steady-state covariance matrix
  7. 1y agocTMedBootstrap centrality estimators and MCPhiSigma()
  8. 4y agohazev0.2.0 -- kdtrees
  9. 4y agohazev0.1.0: Initial release

Frequently asked questions

What is the difference between cTMed and haze?

Both compete on the same themes — r-package — within Analytics. cTMed and haze are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 cTMed better than haze?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cTMed and haze are shipping at a similar cadence (velocity 2.5 vs 2.5, 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 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 haze?

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