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fect vs robma

A side-by-side editorial comparison of fect and robma — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-packageapi-redesign

fect vs robma: at a glance

Featurefectrobma
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesr-package, causal-inference, panel-data, api-redesignr-package, meta-analysis, bayesian, api-redesign
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is fect?

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.

Read the full fect trajectory →

What is robma?

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.

Read the full robma trajectory →

fect vs robma: editorial side-by-side

F
fect
ANALYTICS
0.0

A counterfactual estimator turning itself into a platform for multiple estimands

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

R
robma
ANALYTICS
0.0

RoBMA 4.0 tears out its own constructor surface and rebuilds on one class hierarchy

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

Alternatives to fect and robma

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.

See all fect alternatives → · See all robma alternatives →

Recent activity from fect and robma

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 2mo agofectAdds group.fe for coarsened fixed effects and a $sample slot
  2. 3mo agorobmaUnifies six model constructors into one brma class hierarchy
  3. 3mo agofectPost-hoc estimand API decouples estimands from the fit
  4. 4mo agofectUnified cross-validation and explicit control of time components
  5. 7mo agofectRewrites complex fixed effect handling and fixes speed
  6. 8mo agorobmaRoBMA 3.6.1
  7. 11mo agorobmaRoBMA 3.6.0
  8. 11mo agofectAdds heterogeneous treatment effect plots and caps default cores
  9. 1y agorobmaRoBMA 3.5.1
  10. 1y agorobmaRoBMA 3.5.0
  11. 1y agorobmaRoBMA 3.4.0

Frequently asked questions

What is the difference between fect and robma?

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.

Is fect better than robma?

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.

What are the best alternatives to fect?

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.

What are the best alternatives to robma?

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.