pkglite
pkglite's whole job is knowing which files in an R package are text — and it keeps getting better at guessing.
A side-by-side editorial comparison of fabletools and ggsurvfit — release velocity, themes, recent moves, and the top alternatives to consider.
The tidyverts forecasting core rebuilt model combination on full residual covariance.
fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.
ggsurvfit is in correctness-and-compatibility mode, not feature mode.
The package draws survival and cumulative-incidence curves on a ggplot2 grammar, with risk tables, p-values and quantile annotations. Recent releases are entirely fixes and upstream tracking: ggplot2 v4.0.0 compatibility in 2025, and a 2026 patch correcting a Gray-test p-value that could be reported for the wrong competing event.
fabletools is the framework layer under fable and fpp3 — mables, fables, accuracy measures, reconciliation, and the model arithmetic that lets forecasters express ensembles as expressions. Version 0.8.0 reworked that arithmetic: combination now uses a joint N-way convolution accounting for the full residual covariance across components rather than composing pairwise, and every arithmetic operator collapses to a single model_combination with correctly implied weights, so nested expressions like ((m1 + m2)/2 + m3)/2 flatten automatically. In parallel, the package has been shedding graphics to {ggtime} on a deliberately slow deprecation clock.
The framework is being narrowed and deepened at the same time. Narrowed, because plotting is moving out to a dedicated package over an announced two-year deprecation, leaving fabletools to modeling infrastructure. Deepened, because the recent statistical work targets correctness in places users could not easily inspect — combination weights, inverse-variance weighting computed on response rather than innovation residuals, reconciliation coherency matrices exposed via coherent_smat() and coherent_cmat(). Class hygiene follows the same instinct, with mdl_lst replacing lst_mdl and gaining augment(), glance(), and tidy() so global and reconciliation models report statistics like any other.
With combination and reconciliation infrastructure freshly reworked, the remaining announced work is the ggtime separation, so expect the graphics re-exports to keep degrading toward removal while modeling changes stay incremental.
The package draws survival and cumulative-incidence curves on a ggplot2 grammar, with risk tables, p-values and quantile annotations. Recent releases are entirely fixes and upstream tracking: ggplot2 v4.0.0 compatibility in 2025, and a 2026 patch correcting a Gray-test p-value that could be reported for the wrong competing event.
The feature surface settled around 1.0.0, when risk-table alignment was exported and colour and linetype defaults became configurable. Since then the work is keeping pace with survival, ggplot2 and tidycmprsk changes, and closing cases where the plotted curve and the annotation disagreed — the p-value matched by position rather than name, confidence limits swapped for multi-state models, quantiles read off a plateau.
Expect the next release to track upstream survival or ggplot2 changes rather than add plotting features; the CDISC censoring convention adopted in Surv_CNSR() suggests further alignment with clinical data standards is the likelier direction.
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 fabletools or ggsurvfit.
pkglite's whole job is knowing which files in an R package are text — and it keeps getting better at guessing.
gMCPLite exists to be gMCP without Java, and its releases guard that boundary rather than extend it.
Extracted from Quarto's CLI, typst-gather learned to explain a dependency tree before fetching it.
naijR is assembling the Nigerian reference data R analysts otherwise hand-code every time.
RBesT is teaching its Bayesian decision rules to answer two-sided questions.
graphicalMCP is a narrow statistical tool being hardened rather than grown.
See all fabletools alternatives → · See all ggsurvfit alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. ggsurvfit 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. ggsurvfit 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.
Top fabletools alternatives in Analytics are ranked by recent ship velocity. Browse the "fabletools alternatives" section above for the current picks, or visit /alternatives/fabletools for the full list with editorial commentary on each.
Top ggsurvfit alternatives in Analytics are ranked by recent ship velocity. Browse the "ggsurvfit alternatives" section above for the current picks, or visit /alternatives/ggsurvfit for the full list with editorial commentary on each.