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gratia vs haze

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

Shared themes:r-package

gratia vs haze: at a glance

Featuregratiahaze
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesgam, mgcv, ggplot2, statistical-graphicsneuroimaging, mesh-processing, interpolation, r-package
Last editorial update47m ago6h ago
WebsiteVisit →Visit →

What is gratia?

The tidy front-end for GAMs, now stable enough that upstream ggplot2 sets its release calendar.

gratia wraps mgcv-fitted generalized additive models in tidy data frames and ggplot2 graphics — smooth_estimates(), fitted_values(), derivatives(), draw() and appraise() cover evaluation, prediction and diagnostics. The API reached its intended shape at 0.9.0, when every generated column was renamed to a dot-prefixed form, and 0.10.0 added conditional_values() for covariate-conditional prediction plots.

Read the full gratia 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 →

gratia vs haze: editorial side-by-side

G
gratia
ANALYTICS
0.0

The tidy front-end for GAMs, now stable enough that upstream ggplot2 sets its release calendar.

◆ Current state

gratia wraps mgcv-fitted generalized additive models in tidy data frames and ggplot2 graphics — smooth_estimates(), fitted_values(), derivatives(), draw() and appraise() cover evaluation, prediction and diagnostics. The API reached its intended shape at 0.9.0, when every generated column was renamed to a dot-prefixed form, and 0.10.0 added conditional_values() for covariate-conditional prediction plots.

◆ Where it's heading

The package has moved through a long rewrite cycle and out the other side. Successive releases replaced evaluate_smooth() with smooth_estimates(), rebuilt draw() on top of it, then renamed the entire output vocabulary to avoid colliding with user variables. That work is finished; 0.11.1 is driven almost entirely by ggplot2 4.0.0 compatibility, with new mgcv family support for quantile residuals riding along. Development now tracks upstream breakage rather than internal redesign.

◆ Prediction

Expect the next releases to continue absorbing ggplot2 4.x and mgcv changes, with incremental family coverage in quantile_residuals() as the visible new work. The entries give no signal on which mgcv families come next.

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 gratia 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 gratia or haze.

See all gratia alternatives → · See all haze alternatives →

Recent activity from gratia and haze

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

  1. 18d agohazeVersion 0.3.0 -- Fixes and modernization
  2. 11mo agogratiaggplot2 4.0.0 compatibility, plus four more quantile-residual families
  3. 1y agogratiaconditional_values() replaces vis.gam for conditional prediction plots
  4. 2y agogratiaparametric_effects() joins the dot-prefix rename; LSS families begin
  5. 2y agogratiaEvery generated column gains a dot prefix
  6. 3y agogratiaReal variable names in smooth_samples(), plus dplyr 1.1.0 fixes
  7. 4y agogratiaM1 example output fixes and confint tibble returns
  8. 4y agohazev0.2.0 -- kdtrees
  9. 4y agohazev0.1.0: Initial release

Frequently asked questions

What is the difference between gratia and haze?

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

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

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