TidyDensity
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
A side-by-side editorial comparison of abclass and rfm — release velocity, themes, recent moves, and the top alternatives to consider.
abclass built out angle-based classifiers in 2022, then went quiet except for CRAN upkeep.
An implementation of multi-category angle-based large-margin classifiers with regularization. The capability was assembled in four releases across 2022: group lasso, then group SCAD and MCP penalties, then sparse matrix input, cross-validation via cv.abclass(), an efficient tuning path in et.abclass(), and experimental sup-norm classifiers. After a three-year gap, 0.5.0 simplified how group penalties are specified and 0.5.1 swapped the quadratic programming backend after qpmadr was archived on CRAN.
A customer segmentation package that went quiet for six years and returned with dependency hygiene
rfm computes recency, frequency and monetary segmentation for customer analytics in R. The feature surface was set early: 0.1.0 shipped a Shiny app and customer-level input, 0.2.0 added default segments and median statistics, 0.2.1 added user-specified score thresholds and returnable plot objects. Then nothing for nearly six years. Version 0.4.0 in April 2026 fixes a missing-column error and a customer id fault, and moves plotly and gganimate from Suggests to Imports.
An implementation of multi-category angle-based large-margin classifiers with regularization. The capability was assembled in four releases across 2022: group lasso, then group SCAD and MCP penalties, then sparse matrix input, cross-validation via cv.abclass(), an efficient tuning path in et.abclass(), and experimental sup-norm classifiers. After a three-year gap, 0.5.0 simplified how group penalties are specified and 0.5.1 swapped the quadratic programming backend after qpmadr was archived on CRAN.
The methods surface is complete and the package has moved into maintenance, where releases are triggered by the R ecosystem rather than by research. The one structural habit worth noting is a willingness to change defaults — alpha, epsilon, lum_c and now the cross-validation alignment have all shifted between versions, so results are not stable across upgrades unless arguments are set explicitly.
Expect further releases to track CRAN dependency changes, as 0.5.1 did within a day of qpmadr's archival; nothing in the entries points to new penalty families.
rfm computes recency, frequency and monetary segmentation for customer analytics in R. The feature surface was set early: 0.1.0 shipped a Shiny app and customer-level input, 0.2.0 added default segments and median statistics, 0.2.1 added user-specified score thresholds and returnable plot objects. Then nothing for nearly six years. Version 0.4.0 in April 2026 fixes a missing-column error and a customer id fault, and moves plotly and gganimate from Suggests to Imports.
The 0.4.0 release says more about maintenance posture than about product direction — the version jump past 0.3.x with only two bug fixes and a dependency reshuffle suggests a package being brought back to a releasable state rather than resuming development. Promoting plotly and gganimate to Imports makes the visualization stack mandatory, which is a heavier install in exchange for a simpler code path. The core RFM computation itself has not changed in this window.
The entries show a package returning from dormancy rather than pursuing a roadmap, so further small fixes are more likely than new segmentation capability.
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 abclass or rfm.
A distribution catalogue that grows by one family at a time, and rarely breaks anything.
College football's open data client hit v2 — and now reports how many API calls you have left.
The USA phenology data client rebuilt its entire stack and stopped handing users -9999 as a number.
GeneNMF rebuilt how it derives meta-programs, changing every result it had produced.
Publication-ready psychology tables and plots, tracking APA style as closely as the software allows.
A spatial-statistics utility package exists to be depended on, and is built accordingly.
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. abclass and rfm 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. abclass and rfm 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 abclass alternatives in Analytics are ranked by recent ship velocity. Browse the "abclass alternatives" section above for the current picks, or visit /alternatives/abclass for the full list with editorial commentary on each.
Top rfm alternatives in Analytics are ranked by recent ship velocity. Browse the "rfm alternatives" section above for the current picks, or visit /alternatives/rfm for the full list with editorial commentary on each.