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Comparison · Infra & APIs

JointFPM vs robscale

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

JointFPM vs robscale: at a glance

FeatureJointFPMrobscale
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themessurvival-analysis, recurrent-events, parametric-models, api-stabilityrobust-statistics, simd, performance, cran
Last editorial update37m ago23h ago
WebsiteVisit →Visit →

What is JointFPM?

Recurrent-event modelling settles, with mean_no() promoted to stable.

JointFPM fits joint flexible parametric models for a recurrent event process alongside a competing terminal event, and predicts the mean number of events. The visible history runs from bug fixes on the earliest CRAN releases through standardization, integration options and a summary method, ending with mean_no() declared stable. Several changes arrived through outside pull requests.

Read the full JointFPM trajectory →

What is robscale?

A robust-statistics package rewrote its estimators in SIMD C++ and went to CRAN in two weeks.

robscale computes robust scale and location estimators, and its pitch is speed: 21 to 26 times faster than stats::mad, 37 times faster than stats::IQR on small samples, with comparable margins over robustbase for Qn and Sn. The March 2026 releases took it from a GitHub project to a CRAN package carrying eleven estimators, all with confidence intervals.

Read the full robscale trajectory →

JointFPM vs robscale: editorial side-by-side

J
JointFPM
INFRA · APIS
0.0

Recurrent-event modelling settles, with mean_no() promoted to stable.

◆ Current state

JointFPM fits joint flexible parametric models for a recurrent event process alongside a competing terminal event, and predicts the mean number of events. The visible history runs from bug fixes on the earliest CRAN releases through standardization, integration options and a summary method, ending with mean_no() declared stable. Several changes arrived through outside pull requests.

◆ Where it's heading

The arc runs from a working estimator toward a usable one: input validation and error messages first, then control over the numerical integration, then a summary method and pass-through arguments to the underlying rstpm2 fit. The latest release adds no code so much as a stability commitment to a function users were already calling.

◆ Prediction

With mean_no() stable, the next work most likely targets the prediction and standardization paths rather than the model fit itself.

R
robscale
INFRA · APIS
0.0

A robust-statistics package rewrote its estimators in SIMD C++ and went to CRAN in two weeks.

◆ Current state

robscale computes robust scale and location estimators, and its pitch is speed: 21 to 26 times faster than stats::mad, 37 times faster than stats::IQR on small samples, with comparable margins over robustbase for Qn and Sn. The March 2026 releases took it from a GitHub project to a CRAN package carrying eleven estimators, all with confidence intervals.

◆ Where it's heading

Three releases in a fortnight walk a clear line: expand the public API, submit to CRAN, then tune. The 0.5.4 work is where that tuning shows, and it is unusually specific about hardware, raising sorting-network thresholds after benchmarking and dropping the AVX-512 path entirely in favour of a shorter AVX2-first dispatch chain. The build-fix lists are long, which is what a package fighting compiler and TBB variation across CRAN's platforms looks like.

◆ Prediction

The dispatch hierarchy has been simplified once already; further releases most likely continue narrowing the SIMD surface and hardening the configure step rather than adding estimators. A new estimator would be the surprise.

Alternatives to JointFPM and robscale

Other Infra & APIs 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 JointFPM or robscale.

See all JointFPM alternatives → · See all robscale alternatives →

Recent activity from JointFPM and robscale

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

  1. 4mo agorobscaleSIMD median networks land; AVX-512 dispatch is dropped
  2. 4mo agorobscaleCRAN submission ships 11 robust estimators with bootstrap intervals
  3. 5mo agorobscalescale_robust() dispatcher and four new estimators join the public API
  4. 1y agoJointFPMmean_no() promoted to a stable interface
  5. 2y agoJointFPMsummary() method and control arguments passed to rstpm2
  6. 2y agoJointFPMGaussian quadrature option for the mean-events integration
  7. 2y agoJointFPMStandardized marginal estimates plus input validation
  8. 2y agoJointFPMBug fixes for differences between mean-event functions

Frequently asked questions

What is the difference between JointFPM and robscale?

They serve adjacent needs but don't currently overlap on shipped themes. JointFPM and robscale 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 JointFPM better than robscale?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. JointFPM and robscale 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 Infra & APIs products to evaluate alongside.

What are the best alternatives to JointFPM?

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

What are the best alternatives to robscale?

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