cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
A side-by-side editorial comparison of BORG and common — release velocity, themes, recent moves, and the top alternatives to consider.
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.
A base-R utility belt that grows one small function at a time, on no particular schedule
common collects small helpers that base R leaves out — data frame labelling and sorting, infix operators for pasting and equality, UTF-8 superscript and subscript lookups, file and directory search, attribute copying between data frames. It has no dependencies to speak of and deliberately removed the one it had. In practice it is the shared substrate for its author's wider package family, and its source.all() function is maintained specifically to cooperate with the logr logging package.
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.
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.
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.
common collects small helpers that base R leaves out — data frame labelling and sorting, infix operators for pasting and equality, UTF-8 superscript and subscript lookups, file and directory search, attribute copying between data frames. It has no dependencies to speak of and deliberately removed the one it had. In practice it is the shared substrate for its author's wider package family, and its source.all() function is maintained specifically to cooperate with the logr logging package.
The pattern is accretion rather than direction: each release adds a couple of utilities and fixes whatever the last batch broke, with gaps of a year or more between them. Function additions cluster around whatever the author's other packages needed at the time — file search, attribute preservation, group-boundary detection, script sourcing. The 2025 release continues exactly this, extending the infix comparison operators from equality alone to the full set of relational tests.
Expect more of the same shape — a handful of small helpers whenever a sibling package needs them — with no sign in these entries of a broader API push.
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 common.
A choice-based IRT model published once in 2019 and kept compiling ever since
The R engine behind MetaboAnalyst closes the gap from raw spectra to biological interpretation
Rebuilding SAS's formatting layer in R, one format specification at a time
Standardised coefficients for models where standardising everything is wrong — but the feed only links out
Stream-network spatial models learning to run on data that no longer fits in memory
Bioconductor's installer, frozen at 1.30.x and tuned almost entirely through environment variables
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. BORG and common 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. BORG and common 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 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.
Top common alternatives in Analytics are ranked by recent ship velocity. Browse the "common alternatives" section above for the current picks, or visit /alternatives/common for the full list with editorial commentary on each.