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 forecasting — 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.
HIDDA.forecasting is a book chapter's reproducibility artifact, not a package under development.
HIDDA.forecasting accompanies a book chapter on forecasting infectious disease counts; its vignettes reproduce the results presented there using arima, prophet, glarma, hhh4contacts and scoringRules. The 1.0.0 release states this outright — it is the version used for the chapter, pinned to CRAN package versions as of July 2018. Every release since has been a vignette rebuild against newer R and dependency versions.
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.
HIDDA.forecasting accompanies a book chapter on forecasting infectious disease counts; its vignettes reproduce the results presented there using arima, prophet, glarma, hhh4contacts and scoringRules. The 1.0.0 release states this outright — it is the version used for the chapter, pinned to CRAN package versions as of July 2018. Every release since has been a vignette rebuild against newer R and dependency versions.
The release pattern is maintenance on an eight-year cadence dictated entirely by the surrounding ecosystem: 1.1.1 rebuilt under R 4.0.4, 1.1.2 under R 4.3.2, 1.1.3 under R 4.6.1, each reporting whether the numbers moved. They mostly have not — the recurring note is minor numerical differences confined to the prophet forecasts in vignette('CHILI_prophet'). The only substantive change in the visible history is 1.1.0's methodological tidy-up of the scoring comparisons.
Nothing in these entries points to new functionality; the next release is most likely another vignette rebuild whenever a dependency change or a CRAN check failure forces one.
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 forecasting.
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 forecasting alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. cfbfastr and forecasting 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 forecasting 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 forecasting alternatives in Analytics are ranked by recent ship velocity. Browse the "forecasting alternatives" section above for the current picks, or visit /alternatives/forecasting for the full list with editorial commentary on each.