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Pattern fills for ggplot2, hardened against the ways users write sizes
A side-by-side editorial comparison of fect and robma — release velocity, themes, recent moves, and the top alternatives to consider.
A counterfactual estimator turning itself into a platform for multiple estimands
fect implements counterfactual estimators for panel data with treatment effects — imputation-based fixed effects, interactive fixed effects, matrix completion. The 2026 releases move fast and bundle heavily: 2.1.0 rewrote complex fixed effect handling, 2.2.0 unified cross-validation under a single cv.method parameter and replaced method='gsynth' with an explicit time.component.from switch, 2.4.1 introduced a post-hoc estimand API, and 2.4.5 added group.fe for coarsened fixed effects plus a $sample slot exposing which cells entered estimation.
RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
fect implements counterfactual estimators for panel data with treatment effects — imputation-based fixed effects, interactive fixed effects, matrix completion. The 2026 releases move fast and bundle heavily: 2.1.0 rewrote complex fixed effect handling, 2.2.0 unified cross-validation under a single cv.method parameter and replaced method='gsynth' with an explicit time.component.from switch, 2.4.1 introduced a post-hoc estimand API, and 2.4.5 added group.fe for coarsened fixed effects plus a $sample slot exposing which cells entered estimation.
The direction is separation of estimation from interpretation. Where the package once returned one effect from one fit, estimand() now dispatches typed estimands — ATT, cumulative ATT, APTT, log ATT — from any imputation fit, with effect() and att.cumu() soft-deprecated but byte-identical pending 3.0.0. Alongside that runs a transparency thread: the $sample matrix, out-of-sample comparison via fect_mspe(), and named component sources instead of opaque method aliases. The release notes are unusually precise about which results change and which do not.
The soft-deprecation notice names 3.0.0 as the removal point for effect() and att.cumu(), so a major release consolidating on the estimand() dispatcher is the clearly signposted next step.
RoBMA fits robust Bayesian model-averaged meta-analyses that adjust for publication bias. The 3.x line grew by accretion: separate constructors for each model family (RoBMA.reg, NoBMA, BiBMA and their .reg variants), a spike-and-slab algorithm in 3.3.0 that made estimation fast enough to matter, then a steady stream of post-estimation tooling gated on that algorithm — heterogeneity summaries, residuals, funnel plots, z-curve conversion, predict, extract, pooled and adjusted effects. Version 4.0.0 in May 2026 collapses all of it into a unified brma class hierarchy.
The 3.x series solved the modeling problem and left an interface problem behind: a caller had to know which of six constructors matched their data type, and argument names differed across them. 4.0.0 resolves that by making the model family a set of arguments rather than a function name, and by standardizing input naming on metafor-style conventions. It shipped one day after BayesTools 0.3.0, the author's own upstream infrastructure package, whose new standardization and prior-transformation machinery this rewrite depends on.
A rewrite this wide usually needs a follow-up, so expect 4.0.x patches addressing migration gaps as users hit the removed constructors and renamed arguments.
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 fect or robma.
Pattern fills for ggplot2, hardened against the ways users write sizes
gcube's recent releases are all packaging metadata, not simulation code
The R port of Quinlan's Cubist gets reproducibility fixes, not new modelling
ggstats keeps widening what a coefficient or Likert plot can be
ecodive rebuilt itself into a broad diversity-metric library, breaking as it went
State-space data simulation for R, filled in one function at a time
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package, api-redesign — within Analytics. fect and robma 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. fect and robma 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 fect alternatives in Analytics are ranked by recent ship velocity. Browse the "fect alternatives" section above for the current picks, or visit /alternatives/fect for the full list with editorial commentary on each.
Top robma alternatives in Analytics are ranked by recent ship velocity. Browse the "robma alternatives" section above for the current picks, or visit /alternatives/robma for the full list with editorial commentary on each.