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Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
Comparison · Analytics
gratia vs reliagrowr
A side-by-side editorial comparison of gratia and reliagrowr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
gratia vs reliagrowr: at a glance
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.
What is reliagrowr?
A reliability growth package put its models behind an MCP server for AI assistants to call.
ReliaGrowR fits reliability growth models to failure data — Crow-AMSAA and Duane, with maximum likelihood estimation, confidence bounds, prediction, and reliability demonstration test planning. The last year widened it well past growth curves into repairable systems: parametric non-homogeneous Poisson process fitting with automatic change point detection, non-parametric mean cumulative function estimation, and system exposure calculation. The most recent release adds goodness-of-fit statistics and exposes the package's functions as Model Context Protocol tools.
gratia vs reliagrowr: editorial side-by-side
gratia
0.0ANALYTICS
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.
reliagrowr
0.0ANALYTICS
A reliability growth package put its models behind an MCP server for AI assistants to call.
◆ Current state
ReliaGrowR fits reliability growth models to failure data — Crow-AMSAA and Duane, with maximum likelihood estimation, confidence bounds, prediction, and reliability demonstration test planning. The last year widened it well past growth curves into repairable systems: parametric non-homogeneous Poisson process fitting with automatic change point detection, non-parametric mean cumulative function estimation, and system exposure calculation. The most recent release adds goodness-of-fit statistics and exposes the package's functions as Model Context Protocol tools.
◆ Where it's heading
Two arcs run in parallel. The statistical one is a steady march from plotting a growth curve to modelling recurrent failures properly — segmented NHPP models that detect their own change points, Nelson-Aalen estimation, Cramér-von Mises and Kolmogorov-Smirnov statistics for judging the fits. The interface one is newer and more unusual: the package now ships an MCP server, and its sibling plotting package followed with one two weeks later, so this is a deliberate direction across the maintainer's reliability suite rather than a single experiment. Naming and S3 conventions were cleaned up early, which is what made a uniform tool surface plausible later.
◆ Prediction
Given the sibling packages moved to MCP within weeks of each other, the remaining tools in the suite are the obvious next candidates; on the statistical side, goodness-of-fit having just arrived suggests model comparison and selection helpers are the natural follow-on.
Alternatives to gratia and reliagrowr
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 reliagrowr.
ggtrace
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
kde1d
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
svines
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
gghighlight
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
rtrek
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all gratia alternatives → · See all reliagrowr alternatives →
Recent activity from gratia and reliagrowr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agoreliagrowrReliability growth models exposed as MCP tools ⚡
- 4mo agoreliagrowrRepairable systems analysis arrives: NHPP, MCF, exposure
- 4mo agoreliagrowrMaximum likelihood fitting and failure simulation
- 8mo agoreliagrowrReliaGrowR 0.3.2
- 9mo agoreliagrowrMore plotting and printing options for RGA and Duane models
- 10mo agoreliagrowrS3 methods replace the ad hoc plotting functions
- 11mo agogratiaggplot2 4.0.0 compatibility, plus four more quantile-residual families
- 1y agogratiaconditional_values() replaces vis.gam for conditional prediction plots
- 2y agogratiaparametric_effects() joins the dot-prefix rename; LSS families begin
- 2y agogratiaEvery generated column gains a dot prefix ⚡
- 3y agogratiaReal variable names in smooth_samples(), plus dplyr 1.1.0 fixes
- 4y agogratiaM1 example output fixes and confint tibble returns
Frequently asked questions
What is the difference between gratia and reliagrowr?
Both compete on the same themes — r-package — within Analytics. gratia and reliagrowr 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 gratia better than reliagrowr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. gratia and reliagrowr 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 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 reliagrowr?
Top reliagrowr alternatives in Analytics are ranked by recent ship velocity. Browse the "reliagrowr alternatives" section above for the current picks, or visit /alternatives/reliagrowr for the full list with editorial commentary on each.