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abclass vs intsurv

A side-by-side editorial comparison of abclass and intsurv — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:regularization

abclass vs intsurv: at a glance

Featureabclassintsurv
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesclassification, regularization, large-margin classifiers, cran maintenancesurvival-analysis, cure-models, censored-data, regularization
Last editorial update1h ago43m ago
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What is abclass?

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.

Read the full abclass trajectory →

What is intsurv?

A Cox cure-rate model package woke up after four years to simplify its own interface.

intsurv fits Cox cure rate models for right-censored survival data where event status may be uncertain — the case where you cannot tell whether a subject experienced the event or was never susceptible to it. The core has been stable since 2019: cox_cure() and its regularized counterpart cox_cure_net(), plus a weighted concordance index and a data simulator. After more than four years without a release, version 0.3.0 arrived in September 2025 and restructured how those two functions are configured rather than adding capability.

Read the full intsurv trajectory →

abclass vs intsurv: editorial side-by-side

A
abclass
ANALYTICS
0.0

abclass built out angle-based classifiers in 2022, then went quiet except for CRAN upkeep.

◆ Current state

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.

◆ Where it's heading

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.

◆ Prediction

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.

I
intsurv
ANALYTICS
0.0

A Cox cure-rate model package woke up after four years to simplify its own interface.

◆ Current state

intsurv fits Cox cure rate models for right-censored survival data where event status may be uncertain — the case where you cannot tell whether a subject experienced the event or was never susceptible to it. The core has been stable since 2019: cox_cure() and its regularized counterpart cox_cure_net(), plus a weighted concordance index and a data simulator. After more than four years without a release, version 0.3.0 arrived in September 2025 and restructured how those two functions are configured rather than adding capability.

◆ Where it's heading

The package has reached the point where the methods are settled and the remaining work is ergonomics. Moving control parameters, M-step settings and penalty specification into cox_cure.control(), cox_cure.mstep() and cox_cure_net.penalty() follows the established R convention of separating tuning from the model formula, and it arrives long after the arguments accumulated. The C++ headers were placed in inst/include as early as 2019 so other packages could link against them, which suggests the implementation was always intended to be reused.

◆ Prediction

The gap between 0.2.2 and 0.3.0 makes cadence a poor basis for prediction. What the entries do support is that the interface rework is unfinished business rather than a prelude to new methods, so consolidation around the new helper functions is the likelier next step.

Alternatives to abclass and intsurv

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 intsurv.

See all abclass alternatives → · See all intsurv alternatives →

Recent activity from abclass and intsurv

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 7mo agoabclassQuadratic programming backend swapped after CRAN archival
  2. 10mo agoabclassGroup penalty specification simplified
  3. 10mo agointsurvModel configuration moves into dedicated control functions
  4. 3y agoabclassSparse input, cross-validation and efficient tuning added
  5. 4y agoabclassGroup SCAD and MCP penalties added
  6. 4y agoabclassGroup lasso regularization and correctness fixes
  7. 4y agoabclassFirst release of the angle-based classifiers
  8. 5y agointsurvCross-validated model selection and offset terms added
  9. 6y agointsurvC++ headers relocated so other packages can link them
  10. 7y agointsurvCox cure models for uncertain event status arrive
  11. 7y agointsurvParameter initialization methods added to the alpha
  12. 7y agointsurvAlpha cut for paper submission and simulation reproducibility

Frequently asked questions

What is the difference between abclass and intsurv?

Both compete on the same themes — regularization — within Analytics. abclass and intsurv 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 abclass better than intsurv?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. abclass and intsurv 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 abclass?

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.

What are the best alternatives to intsurv?

Top intsurv alternatives in Analytics are ranked by recent ship velocity. Browse the "intsurv alternatives" section above for the current picks, or visit /alternatives/intsurv for the full list with editorial commentary on each.