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abclass vs healthyR.ts

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

Shared themes:r package

abclass vs healthyR.ts: at a glance

FeatureabclasshealthyR.ts
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesclassification, regularization, large-margin classifiers, cran maintenancetime series, healthyverse, stationarity, ggplot2
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

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 healthyR.ts?

healthyR.ts keeps adding time-series helpers, then quietly breaks the old ones to modernise them.

A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.

Read the full healthyR.ts trajectory →

abclass vs healthyR.ts: 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.

H
healthyR.ts
ANALYTICS
0.0

healthyR.ts keeps adding time-series helpers, then quietly breaks the old ones to modernise them.

◆ Current state

A time-series companion in the healthyverse family, shipping helper functions in batches: growth-rate vectors, an ADF test and auto_stationarize() in 0.2.11, then five log and differencing transforms in 0.3.0, and a random-walk plot in 0.3.2. Alongside the additions runs a steady stream of breaking cleanups — invisible returns dropped, R 4.1 required for the native pipe, and ts_ma_plot() refactored onto ggplot2 facets with its xts output removed and its return value cut from six items to two.

◆ Where it's heading

Two threads, both consistent. The functional one is coverage of the stationarity workflow — transform, test, auto-stationarize, plot — assembled function by function rather than as a single API. The structural one is convergence on ggplot2 and tidy conventions, retiring xts objects and multi-object return lists as it goes. The package is not afraid to break return shapes to get there, so upgrades are not drop-in.

◆ Prediction

Expect the remaining functions that still return xts objects or bundled lists to get the same ggplot2-only treatment, since ts_ma_plot() was refactored on exactly that rationale.

Alternatives to abclass and healthyR.ts

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 healthyR.ts.

See all abclass alternatives → · See all healthyR.ts alternatives →

Recent activity from abclass and healthyR.ts

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

  1. 6mo agohealthyR.tsRandom walk plot added; ts_ma_plot drops xts for ggplot2 facets
  2. 7mo agoabclassQuadratic programming backend swapped after CRAN archival
  3. 10mo agoabclassGroup penalty specification simplified
  4. 1y agohealthyR.tsInvisible returns dropped; random walk and vva plot fixes
  5. 2y agohealthyR.tsFive log and differencing transform utilities added
  6. 2y agohealthyR.tsStationarity testing and auto_stationarize added
  7. 2y agohealthyR.tsSingle example fix
  8. 3y agohealthyR.tsBoilerplate fitting uses show_best directly
  9. 3y agoabclassSparse input, cross-validation and efficient tuning added
  10. 4y agoabclassGroup SCAD and MCP penalties added
  11. 4y agoabclassGroup lasso regularization and correctness fixes
  12. 4y agoabclassFirst release of the angle-based classifiers

Frequently asked questions

What is the difference between abclass and healthyR.ts?

Both compete on the same themes — r package — within Analytics. abclass and healthyR.ts 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 healthyR.ts?

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

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