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mGSFPCA

0.2.2Package record

Estimate Functional Principal Components from Sparse Data

0packages depend
2.9Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

Implements functional principal component analysis (FPCA) for univariate and multivariate sparse functional data. The package estimates eigenfunctions, eigenvalues, and error variance simultaneously via maximum likelihood estimation (MLE), using a spline basis representation of the eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced through a modified Gram-Schmidt (MGS) orthogonalization procedure applied iteratively during estimation, avoiding direct optimization over the Stiefel manifold and improving numerical stability. The optimal number of basis functions and principal components is selected via an Akaike Information Criterion (AIC)-type criterion, supporting both a full grid-search strategy and a computationally efficient sequential selection approach. Principal component scores are estimated by conditional expectation, enabling reconstruction of individual trajectories over the entire domain from sparse observations. Pointwise confidence intervals for reconstructed trajectories are also provided. Methods are described in Mbaka, Cao and Carey (2026) doi:10.48550/arXiv.2603.18833 and Mbaka and Carey (2026) doi:10.48550/arXiv.2603.19799.

Install

Health

CRAN checks
13OK
Slowest check: 5.3 min · r-devel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-06-09
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK7 OK2026-05-09
Advisories

none known

Documentation

In release 0.2.2
READMEYes · 21 wordsVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
7
Examples that run
50%2 run in a plain check · 2 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples
40%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
43%

Downloads

2.9K
CRAN downloads in the past year
Rank #4,599 · ~8/day · ~240/mo
Daily download trend is not available in this view yet.
1.3K30 days
1.8K90 days
2.9K1 year
Compare downloads with other packages →
Also on98 r2u3 autocran

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

Installing mGSFPCA adds 38 packages (23 with compiled code, 34 MB of source tarballs). Heaviest: ggplot2 6.0 MB, fds 3.7 MB, mclust 2.7 MB.

Depends (1)
R >= 2.10
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Datasets

People & History

People (2)
Author, Maintainer since 0.2.2 (2026)
Contributor
Releases (1)
  • 0.2.2Latest
    2026-05-08 · current release
  • R
    R 4.6.0 released · 2026-04-24

Package metadata

First published
2026-05-08
Total releases
1 / 1 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 2.10
Bundled data
1.4 MB / 2 files
Download size
1.4 MB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("mGSFPCA")
Mbaka, U., & Carey, M. (2026). mGSFPCA: Estimate Functional Principal Components from Sparse Data (Version 0.2.2) [Computer software]. https://doi.org/10.32614/CRAN.package.mGSFPCA

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 mGSFPCA version 0.2.2 [Data set]. HJJB, LLC. Data release v2026-10-20. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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