JointFPM
Recurrent-event modelling settles, with mean_no() promoted to stable.
A side-by-side editorial comparison of goat and statsExpressions — release velocity, themes, recent moves, and the top alternatives to consider.
A gene-set enrichment package that outgrew its human-only origins, then went quiet.
GOAT is a CRAN-published R package for gene set enrichment testing, now at 1.1.4. The visible arc runs from a 2024 beta through a first public CRAN release to a 1.1 line that broadened the package past human gene sets and added persistence for completed analyses. Recent releases are small: the newest ships an igraph handle on plot_network() plus bug fixes.
A statistics backend whose release history is mostly other people's weather
statsExpressions produces the tidy dataframes and plotmath expressions that ggstatsplot prints onto plots, and that position defines its changelog. Six of its ten most recent versions exist to absorb API changes in easystats, dplyr or purrr. Version 2.0.0 is the exception, cut to accompany ggstatsplot's own 1.0.0 and carrying pairwise Fisher's exact post hocs for contingency tables.
GOAT is a CRAN-published R package for gene set enrichment testing, now at 1.1.4. The visible arc runs from a 2024 beta through a first public CRAN release to a 1.1 line that broadened the package past human gene sets and added persistence for completed analyses. Recent releases are small: the newest ships an igraph handle on plot_network() plus bug fixes.
The substantive expansion happened in the 1.1 cycle; everything since has been maintenance and plotting ergonomics. Each release since 1.1 touches one function and returns something callers previously had to reconstruct, which reads as a package settling into a stable API and responding to individual user requests rather than pursuing new scope. The 13-month gap between 1.1.2 and 1.1.4 puts it firmly in low-cadence maintenance.
Expect continued point releases that expose internals from the plotting functions or refresh the bundled GO release, not new analysis capability. The entries give no signal of a planned 1.2.
statsExpressions produces the tidy dataframes and plotmath expressions that ggstatsplot prints onto plots, and that position defines its changelog. Six of its ten most recent versions exist to absorb API changes in easystats, dplyr or purrr. Version 2.0.0 is the exception, cut to accompany ggstatsplot's own 1.0.0 and carrying pairwise Fisher's exact post hocs for contingency tables.
This is a component settling into place beneath a larger package rather than a product with its own roadmap. New statistical content arrives rarely and narrowly — an exact-p toggle, one post-hoc function — while the recurring work is keeping expressions correct as the easystats stack shifts underneath. The one bug class it keeps returning to is rendering: p-values of exactly zero, decimal commas that plotmath parses as list separators.
Coupled this tightly, the next release is most likely another compatibility pass timed to an easystats or ggstatsplot version rather than new tests. Nothing in these entries signals an independent feature direction.
Other Infra & APIs 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 goat or statsExpressions.
Recurrent-event modelling settles, with mean_no() promoted to stable.
Nonparametric change point detection swaps p-values for importance scores.
A Prism-styled ggplot2 theme in maintenance, now surviving ggplot2 4.0.
Wavelet trend estimation tightens the defaults it shipped with.
Back from CRAN removal under a new maintainer, with the compiled layer rebuilt.
mice can finally predict, not just estimate, from multiply imputed data.
See all goat alternatives → · See all statsExpressions alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — maintenance — within Infra & APIs. goat and statsExpressions 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. goat and statsExpressions 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 Infra & APIs products to evaluate alongside.
Top goat alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "goat alternatives" section above for the current picks, or visit /alternatives/goat for the full list with editorial commentary on each.
Top statsExpressions alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "statsExpressions alternatives" section above for the current picks, or visit /alternatives/statsexpressions for the full list with editorial commentary on each.