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fdaMocca

0.1-2

Model-Based Clustering for Functional Data with Covariates

0packages depend
2.6Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Natalya PyaFirst published 2021-10-213 releasesCRAN page ↗

Routines for model-based functional cluster analysis for functional data with optional covariates. The idea is to cluster functional subjects (often called functional objects) into homogenous groups by using spline smoothers (for functional data) together with scalar covariates. The spline coefficients and the covariates are modelled as a multivariate Gaussian mixture model, where the number of mixtures corresponds to the number of clusters. The parameters of the model are estimated by maximizing the observed mixture likelihood via an EM algorithm (Arnqvist and Sjöstedt de Luna, 2019) doi:10.48550/arXiv.1904.10265. The clustering method is used to analyze annual lake sediment from lake Kassjön (Northern Sweden) which cover more than 6400 years and can be seen as historical records of weather and climate.

Install

Health

CRAN checks
13OK
Slowest check: 1.8 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
9
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-06-09
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-06-08
    12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
75%
Return-value docs
100%
References docs
90%

Downloads

2.6K
CRAN downloads in the past year
Rank #16,060 · ~7/day · ~213/mo
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13930 days
65890 days
2.6K1 year
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Also on123 r2u21 autocran

Dependencies

Declared dependencies
4 external dependencies (excludes base and recommended)
Depends (1)
R >= 4.4.0
Imports (9)
statsgraphicsMatrixparallelforeachdoParallelmvtnormfdagrDevices
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (3)
Maintainer (1)
Author, Maintainer · added in 0.1-2
Authors (3)
Author, Maintainer · added in 0.1-2
Listed in earlier versions (1)
no longer listed · 0.1-0 to 0.1-1
Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • 0.1-2Latest
    2025-03-31 · current release · diff ↗
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • 0.1-1
    2022-07-21 · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • 0.1-0
    2021-10-21
  • R
    R 4.1.0 released · 2021-05-18

Package metadata

First published
2021-10-21
Total releases
3 / 5 yrs
License
GPL (>= 2) OSI
Minimum R
≥ 4.4.0
Bundled data
1.2 MB / 5 files
Download size
1.2 MB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("fdaMocca")
Pya, N., Arnqvist, P., & Sjöstedt de Luna, S. (2025). fdaMocca: Model-Based Clustering for Functional Data with Covariates (Version 0.1-2) [Computer software]. https://doi.org/10.32614/CRAN.package.fdaMocca

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for fdaMocca version 0.1-2 [Data set]. HJJB, LLC. Data release v2026-08-16. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-16, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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