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Comparison · Infra & APIs

CptNonPar vs tf

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

CptNonPar vs tf: at a glance

FeatureCptNonPartf
SectorInfra & APIsInfra & APIs
Velocity score0.00.0
Sparks · 30d00
Top themeschange-point-detection, nonparametric, defaults, preprocessingfunctional-data-analysis, vctrs, multivariate, r-packages
Last editorial update38m ago3h 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 tf?

tf gave functional data a second dimension: curves whose values are vectors.

tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.

Read the full tf trajectory →

CptNonPar vs tf: 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.

T
tf
INFRA · APIS
0.0

tf gave functional data a second dimension: curves whose values are vectors.

◆ Current state

tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.

◆ Where it's heading

The package is widening what a functional observation can be, then porting the toolkit onto it. Registration arrived first in 0.4.0 for univariate curves and immediately gained an srvf_mv method for aligning components jointly, and tfb_mfpc() ports principal component analysis to the multivariate case with a single set of scores shared across components. Alongside that runs steady dependency shedding — mvtnorm and pracma both replaced by inlined samplers that reproduce prior draws bit-for-bit, glue dropped for cli in the previous release — and an unusually long tail of NA-handling and edge-case fixes, several caught in pre-release review of the new classes.

◆ Prediction

The new classes ship with FPCA, registration and shape alignment but the release notes describe tidyfun::tf_unnest() as the consumer of one new export, so the visible next step is the rest of the tidyfun stack catching up to vector-valued columns. Expect follow-up patches on the vctrs casting paths, which is where most of this release's late fixes clustered.

Alternatives to CptNonPar and tf

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 tf.

See all CptNonPar alternatives → · See all tf alternatives →

Recent activity from CptNonPar and tf

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

  1. 1mo agotfVector-valued functional data becomes a first-class type
  2. 5mo agotfCurve registration, five depth measures and sub-domain splitting
  3. 8mo agoCptNonParData centred and scaled by default before detection
  4. 1y agoCptNonParImportance scores replace p-values; per-lag manual thresholds
  5. 2y agotfFix: tf_crosscov normalization
  6. 2y agoCptNonParPaper link updated for CRAN checks
  7. 3y agoCptNonParDescription field and example cleanups

Frequently asked questions

What is the difference between CptNonPar and tf?

They serve adjacent needs but don't currently overlap on shipped themes. CptNonPar and tf 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 tf?

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

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