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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "mGSFPCA" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'mGSFPCA: Estimate Functional Principal Components from Sparse Data'
version: 0.2.2
abstract: 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)
  <https://doi.org/10.48550/arXiv.2603.18833> and Mbaka and Carey (2026) <https://doi.org/10.48550/arXiv.2603.19799>.
authors:
- family-names: Mbaka
  given-names: Uche
  email: uche.mbaka@ucd.ie
  orcid: https://orcid.org/0000-0002-1427-6388
repository: https://cran.r-universe.dev
commit: b0f47c5c46647074f2c61fc11ef4a419d780c030
date-released: '2026-05-08'
contact:
- family-names: Mbaka
  given-names: Uche
  email: uche.mbaka@ucd.ie
  orcid: https://orcid.org/0000-0002-1427-6388