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Comparison · Infra & APIs

CptNonPar vs mice

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

Shared themes:statistics

CptNonPar vs mice: at a glance

FeatureCptNonParmice
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themeschange-point-detection, nonparametric, defaults, preprocessingmissing-data, multiple-imputation, statistics, r-package
Last editorial update35m ago1h ago
WebsiteVisit →Visit →

What is CptNonPar?

Nonparametric change point detection swaps p-values for importance scores.

CptNonPar implements nonparametric MOJO change point detection for possibly multivariate, serially dependent data, through single-lag, multi-lag and multiscale entry points. Recent releases concern how results are reported and how data is preprocessed rather than new detection machinery. The underlying method was accepted at Biometrika during the 0.3.0 cycle.

Read the full CptNonPar trajectory →

What is mice?

mice can finally predict, not just estimate, from multiply imputed data.

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

Read the full mice trajectory →

CptNonPar vs mice: editorial side-by-side

C
CptNonPar
INFRA · APIS
0.0

Nonparametric change point detection swaps p-values for importance scores.

◆ Current state

CptNonPar implements nonparametric MOJO change point detection for possibly multivariate, serially dependent data, through single-lag, multi-lag and multiscale entry points. Recent releases concern how results are reported and how data is preprocessed rather than new detection machinery. The underlying method was accepted at Biometrika during the 0.3.0 cycle.

◆ Where it's heading

The package is tightening the statistical interface it exposes: p-values gave way to importance scores across all three detection functions, manual thresholds became specifiable per lag, and the latest release makes centring and scaling the default preprocessing step. Each change folds a decision the user previously had to make into the package itself.

◆ Prediction

Expect further work on defaults and reporting around the existing MOJO estimators rather than a new detection method.

M
mice
INFRA · APIS
0.0

mice can finally predict, not just estimate, from multiply imputed data.

◆ Current state

mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.

◆ Where it's heading

Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.

◆ Prediction

predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.

Alternatives to CptNonPar and mice

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 CptNonPar or mice.

See all CptNonPar alternatives → · See all mice alternatives →

Recent activity from CptNonPar and mice

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

  1. 8mo agomicemice 3.19.0
  2. 8mo agoCptNonParData centred and scaled by default before detection
  3. 1y agomicemice 3.18.0
  4. 1y agoCptNonParImportance scores replace p-values; per-lag manual thresholds
  5. 1y agomicemice 3.17.0
  6. 2y agoCptNonParPaper link updated for CRAN checks
  7. 3y agoCptNonParDescription field and example cleanups
  8. 3y agomicemice 3.16.0
  9. 3y agomicemice 3.15.0
  10. 4y agomicemice 3.14.0

Frequently asked questions

What is the difference between CptNonPar and mice?

Both compete on the same themes — statistics — within Infra & APIs. CptNonPar and mice 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 CptNonPar better than mice?

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

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

What are the best alternatives to mice?

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