fda.vi: Functional Data Analysis using Variational Inference

Implements a variational Expectation-Maximization (VEM) algorithm for smoothing one or multiple functional observations via basis function selection. The algorithm estimates all model parameters simultaneously and automatically, while accounting for within-curve correlation. The approach provides a flexible and computationally efficient framework for smoothing correlated functional data. The algorithm is described in da Cruz, A. C., de Souza, C. P., and Sousa, P. H. (2024). 'Fast Bayesian basis selection for functional data representation with correlated errors.' <doi:10.48550/arXiv.2405.20758>.

Version:	1.0.0
Depends:	R (≥ 4.1)
Imports:	stats, graphics, fda, MASS, scales
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-06-20
DOI:	10.32614/CRAN.package.fda.vi
Author:	Camila de Souza [cre], Stephen Kinsey [aut], Ana Carolina da Cruz [aut], Pedro Henrique Toledo Oliveira Sousa [aut]
Maintainer:	Camila de Souza <camila.souza at uwo.ca>
BugReports:	https://github.com/desouzalab/fda.vi/issues
License:	MIT + file LICENSE
URL:	https://github.com/desouzalab/fda.vi
NeedsCompilation:	no
Materials:	README
CRAN checks:	fda.vi results

Documentation:

Reference manual:	fda.vi.html , fda.vi.pdf
Vignettes:	Introduction to fda.vi (source, R code)

Downloads:

Package source:	fda.vi_1.0.0.tar.gz
Windows binaries:	r-devel: fda.vi_1.0.0.zip, r-release: fda.vi_1.0.0.zip, r-oldrel: fda.vi_1.0.0.zip
macOS binaries:	r-release (arm64): fda.vi_1.0.0.tgz, r-oldrel (arm64): fda.vi_1.0.0.tgz, r-release (x86_64): fda.vi_1.0.0.tgz, r-oldrel (x86_64): fda.vi_1.0.0.tgz

Linking:

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