Estimate Functional Principal Components from Sparse Data
Overview
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
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK7 OK2026-05-09
Documentation
- 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
Dependencies
Code & Tests
Datasets
People & History
- 0.2.2Latest2026-05-08 · current release
- RR 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")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.
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.