cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
Comparison · Analytics
BORG vs fmtr
A side-by-side editorial comparison of BORG and fmtr — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
BORG vs fmtr: at a glance
What is BORG?
A cross-validation guard that refuses to run random CV on dependent data unless you insist
BORG detects spatial, temporal and clustered dependence in a modelling dataset and generates a cross-validation scheme that respects it — spatial blocks, temporal blocks, group folds — rather than letting random splits leak information between train and test. Its distinguishing choice is enforcement: when it finds dependence, random CV is blocked outright and needs an explicit allow_random=TRUE to proceed. The package also wraps the standard rsample and caret entry points so the guard applies inside existing workflows.
What is fmtr?
Rebuilding SAS's formatting layer in R, one format specification at a time
fmtr applies formats to R data the way SAS applies them: named format catalogues, format lists, and an fapply() that maps a specification onto a vector. It is part of a family of packages that reconstruct SAS reporting idioms in R, and it shares infrastructure with them — labels.data.frame() was moved out to the common package, which fmtr now depends on. The recent releases have been closing specific gaps against SAS's own format vocabulary.
BORG vs fmtr: editorial side-by-side
BORG
0.0ANALYTICS
A cross-validation guard that refuses to run random CV on dependent data unless you insist
◆ Current state
BORG detects spatial, temporal and clustered dependence in a modelling dataset and generates a cross-validation scheme that respects it — spatial blocks, temporal blocks, group folds — rather than letting random splits leak information between train and test. Its distinguishing choice is enforcement: when it finds dependence, random CV is blocked outright and needs an explicit allow_random=TRUE to proceed. The package also wraps the standard rsample and caret entry points so the guard applies inside existing workflows.
◆ Where it's heading
The entire visible history is a single day, and the sequence within it is coherent rather than churn: enforcement first, then the evidence layer, then framework integration, then idiomatic R polish. The evidence work matters to the pitch — borg_compare_cv() runs random against blocked CV so users see the inflation on their own data instead of taking the warning on faith, and the methods-text and certificate exports are aimed squarely at getting this into published papers. By the final release the interface has been rebuilt on standard S3 plot and summary methods.
◆ Prediction
The wrappers so far cover rsample and caret; tidymodels and mlr3 are the obvious remaining entry points if the guard is to reach the workflows it hasn't yet intercepted.
fmtr
0.0ANALYTICS
Rebuilding SAS's formatting layer in R, one format specification at a time
◆ Current state
fmtr applies formats to R data the way SAS applies them: named format catalogues, format lists, and an fapply() that maps a specification onto a vector. It is part of a family of packages that reconstruct SAS reporting idioms in R, and it shares infrastructure with them — labels.data.frame() was moved out to the common package, which fmtr now depends on. The recent releases have been closing specific gaps against SAS's own format vocabulary.
◆ Where it's heading
The direction is parity, pursued in small increments. Quarter format codes were added because base R has none; the SAS best. format was reimplemented, then hardened against the variations people actually write; statistical summary helpers like fmt_mean_sd() and fmt_mean_stderr() cover the cell contents clinical tables need. The structural work is largely behind it, including the breaking 2022 move that handed labelling to a sibling package, so what remains is vocabulary coverage.
◆ Prediction
The pattern of adding a SAS format, then a release to handle its variants, suggests the next releases continue filling in format codes and summary helpers rather than changing how formats are applied.
Alternatives to BORG and fmtr
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 BORG or fmtr.
MetaboAnalystR
The R engine behind MetaboAnalyst closes the gap from raw spectra to biological interpretation
betaselectr
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
SSN2
Stream-network spatial models learning to run on data that no longer fits in memory
BiocManager
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
dcurves
Decision curve analysis, settled since 2022 and now moving only when its neighbours do
Recent activity from BORG and fmtr
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 7mo agoBORGInterface rebuilt on standard S3 plot and summary methods
- 7mo agoBORGGuarded wrappers for rsample and caret splitting functions
- 7mo agoBORGEmpirical inflation comparison and publication-ready reporting
- 7mo agoBORGRandom CV blocked by default when dependence is detected ⚡
- 7mo agoBORGVersion bump to 0.1.1
- 10mo agofmtrMean and standard error helper, plus best-format variants
- 11mo agofmtrSAS best. format reimplemented in fapply()
- 2y agofmtrQuarter format codes %q and %Q added
- 2y agofmtrFormat lists become readable and writable files
- 2y agofmtrDocumentation and examples expanded
- 2y agofmtrvalue() can return results as a factor
Frequently asked questions
What is the difference between BORG and fmtr?
Both compete on the same themes — r-package — within Analytics. BORG and fmtr 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 BORG better than fmtr?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. BORG and fmtr 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 BORG?
Top BORG alternatives in Analytics are ranked by recent ship velocity. Browse the "BORG alternatives" section above for the current picks, or visit /alternatives/borg for the full list with editorial commentary on each.
What are the best alternatives to fmtr?
Top fmtr alternatives in Analytics are ranked by recent ship velocity. Browse the "fmtr alternatives" section above for the current picks, or visit /alternatives/fmtr for the full list with editorial commentary on each.