STACAS
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A side-by-side editorial comparison of cfbfastr and gratia — release velocity, themes, recent moves, and the top alternatives to consider.
College football's open data client hit v2 — and now reports how many API calls you have left.
cfbfastR retrieves college football data — play-by-play, box scores, betting lines, ratings and recruiting — from the CollegeFootballData API, ESPN endpoints and the sportsdataverse data repository. Version 2.0.0 in September 2025 was the first release in over three years and rebuilt the package against CFBD's v2 API. Every load_cfb_*() function changed its underlying source to comply with CFBD's terms, the play-by-play dataset gained team and game identifiers users previously had to join in themselves, and a batch of new endpoints arrived covering opponent-adjusted metrics, FPI ratings and live scoreboard and play data.
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.
cfbfastR retrieves college football data — play-by-play, box scores, betting lines, ratings and recruiting — from the CollegeFootballData API, ESPN endpoints and the sportsdataverse data repository. Version 2.0.0 in September 2025 was the first release in over three years and rebuilt the package against CFBD's v2 API. Every load_cfb_*() function changed its underlying source to comply with CFBD's terms, the play-by-play dataset gained team and game identifiers users previously had to join in themselves, and a batch of new endpoints arrived covering opponent-adjusted metrics, FPI ratings and live scoreboard and play data.
The package's direction is now set by the data provider rather than by its own plans, and that provider has moved to metered access — the free tier is capped at 1,000 calls a month, with limits tied to membership level. cfbd_api_key_info() reporting a user's tier and usage is the clearest sign of that shift: quota is now something an analysis has to manage. The long gap before 2.0.0 and its arrival largely through a first-time contributor also indicate a package sustained by community effort rather than steady maintenance.
The live scoreboard and play endpoints are the natural place for the next work, since they are the ones that benefit from in-season iteration. Given the release notes warn users to check their pipelines, follow-up fixes for the changed loading functions are likely before anything new lands.
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.
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.
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.
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 cfbfastr or gratia.
Single-cell batch correction that learned to use cell labels, then spent three releases chasing Seurat.
A debugger for ggplot2's internals, hardening its grip as the internals it traces keep moving.
A univariate density estimator that added zero-inflated data and reopened its C++ API to do it.
Stationary vine copulas for time series, released in lockstep with the rest of Nagler's vine stack.
A single-purpose ggplot2 extension that has spent six years tracking ggplot2 instead of growing.
A Star Trek data package that became a Memory Alpha web client and has been patching scrapers ever since.
See all cfbfastr alternatives → · See all gratia alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. cfbfastr and gratia 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cfbfastr and gratia 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.
Top cfbfastr alternatives in Analytics are ranked by recent ship velocity. Browse the "cfbfastr alternatives" section above for the current picks, or visit /alternatives/cfbfastr for the full list with editorial commentary on each.
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.