cIRT
A choice-based IRT model published once in 2019 and kept compiling ever since
A side-by-side editorial comparison of fmtr and MultiSpline — release velocity, themes, recent moves, and the top alternatives to consider.
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.
MultiSpline went from five functions to a full multilevel spline framework in seven weeks.
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
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.
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.
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.
MultiSpline fits spline-based nonlinear models to multilevel and longitudinal data in R. The package reached CRAN in February 2026 with five functions covering fitting, summary, prediction, plotting and intraclass correlations. Version 0.2.0, seven weeks later, adds cross-classified and nested random-effect structures, automatic knot selection, a multilevel R-squared variance partition, derivative-based interpretation with turning points, model comparison against polynomials, and cluster heterogeneity analysis, while keeping every 0.1.0 call valid.
The arc is a research package being built out into a workflow at speed: 0.1.0 could fit a curve, 0.2.0 can tell you where the curve turns, how much variance each level explains, and whether a spline beats a polynomial at all. Backward compatibility was preserved across that expansion, which suggests the author is building for outside users rather than a single paper. The JOSS submission referenced in 0.1.1 points at academic distribution as the intended channel.
With the interpretation and diagnostics layers now in place, the next release will most likely extend the supported model families beyond the current lmer and glmer backends.
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 fmtr or MultiSpline.
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
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
Decision curve analysis, settled since 2022 and now moving only when its neighbours do
See all fmtr alternatives → · See all MultiSpline alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. fmtr and MultiSpline 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. fmtr and MultiSpline 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 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.
Top MultiSpline alternatives in Analytics are ranked by recent ship velocity. Browse the "MultiSpline alternatives" section above for the current picks, or visit /alternatives/multispline for the full list with editorial commentary on each.