Skip to content

mGSFPCA

0.2.2

Estimate Functional Principal Components from Sparse Data

0packages depend
1.3Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Uche MbakaFirst published 2026-05-081 releasesCRAN page ↗

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: 5.7 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
4
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
  • OK2026-05-09
    7 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 21 wordsVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
50%
Documented parameters
100%
Return-value docs
100%
References docs
43%

Downloads

1.3K
CRAN downloads in the past year
Rank #10,421 · ~3/day · ~105/mo
Daily download trend is not available in this view yet.
17130 days
92090 days
1.3K1 year
Compare downloads with other packages →
Also on82 r2u

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)
Depends (1)
R >= 2.10
LinkingTo (2)
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (2)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Contributors (1)
Contributor
Package Timeline

1 release. R releases are shown for context.

  • 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
Installed size
not tracked yet
With dependencies
not tracked yet

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 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 mGSFPCA version 0.2.2 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy