nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
fect vs relialearnr
A side-by-side editorial comparison of fect and relialearnr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
fect vs relialearnr: at a glance
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.
What is relialearnr?
The teaching arm of an R reliability suite keeps pace with whatever its analysis siblings ship.
ReliaLearnR is a set of interactive learnr tutorials for reliability engineering, covering life data analysis, reliability testing, RAM concepts, reliability block diagrams, and repairable systems, each with code exercises and quiz questions. It was WeibullR.learnr until the start of 2026, when the rename and a set of shorter function names arrived together. A companion book now supplements the interactive material.
fect vs relialearnr: editorial side-by-side
fect
0.0ANALYTICS
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.
relialearnr
0.0ANALYTICS
The teaching arm of an R reliability suite keeps pace with whatever its analysis siblings ship.
◆ Current state
ReliaLearnR is a set of interactive learnr tutorials for reliability engineering, covering life data analysis, reliability testing, RAM concepts, reliability block diagrams, and repairable systems, each with code exercises and quiz questions. It was WeibullR.learnr until the start of 2026, when the rename and a set of shorter function names arrived together. A companion book now supplements the interactive material.
◆ Where it's heading
The tutorials track the maintainer's analysis packages rather than leading them: repairable systems and mean cumulative function teaching material appeared once the modelling functions for them existed elsewhere in the suite, and the reliability testing tutorial followed the same pattern earlier. Recent work has been about depth rather than coverage — interactive parameter sliders, goodness-of-fit sections, model comparison exercises, more quiz questions per topic. The rename to ReliaLearnR was part of the same suite-wide repositioning away from Weibull-specific branding that the plotting package made.
◆ Prediction
On the established pattern, the next tutorials will follow whatever the analysis packages shipped most recently; the entries do not indicate whether the newer tool-server interfaces will get teaching material of their own.
Alternatives to fect and relialearnr
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 relialearnr.
susier
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
UCell
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
detectseparation
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
brglm2
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
bayestools
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
See all fect alternatives → · See all relialearnr alternatives →
Recent activity from fect and relialearnr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agofectAdds group.fe for coarsened fixed effects and a $sample slot
- 2mo agorelialearnrBlock diagram and repairable systems tutorials added
- 3mo agofectPost-hoc estimand API decouples estimands from the fit ⚡
- 4mo agofectUnified cross-validation and explicit control of time components
- 7mo agofectRewrites complex fixed effect handling and fixes speed
- 7mo agorelialearnrReliaLearnR 0.3.1
- 7mo agorelialearnrRenamed to ReliaLearnR, with shorter tutorial launchers
- 11mo agofectAdds heterogeneous treatment effect plots and caps default cores
- 1y agorelialearnrWeibullR.learnr 0.2.1
- 1y agorelialearnrReliability testing tutorial covering growth analysis and ALT
- 3y agorelialearnrFirst release: the life data analysis tutorial
Frequently asked questions
What is the difference between fect and relialearnr?
Both compete on the same themes — r-package — within Analytics. fect and relialearnr 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 relialearnr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. fect and relialearnr 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 relialearnr?
Top relialearnr alternatives in Analytics are ranked by recent ship velocity. Browse the "relialearnr alternatives" section above for the current picks, or visit /alternatives/relialearnr for the full list with editorial commentary on each.