Package: fda.vi 1.0.0

Camila de Souza

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>.

Authors:Camila de Souza [cre], Stephen Kinsey [aut], Ana Carolina da Cruz [aut], Pedro Henrique Toledo Oliveira Sousa [aut]

fda.vi_1.0.0.tar.gz
fda.vi_1.0.0.tar.gz(r-4.7-any)fda.vi_1.0.0.tar.gz(r-4.6-any)
fda.vi_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
fda.vi/json (API)

# Install 'fda.vi' in R:
install.packages('fda.vi', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/desouzalab/fda.vi/issues

Datasets:

On CRAN:

Conda:

3.70 score 34 mentions 4 exports 43 dependencies

Last updated from:df608237a8. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK133
source / vignettesOK227
linux-release-x86_64OK127
wasm-releaseOK113

Exports:gcv_vemtune_vem_by_gcvvem_fitvem_smooth

Dependencies:ashbitopscliclustercolorspacecpp11deSolvefarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmaR6rainbowRColorBrewerRcppRCurlrlangS7scalesvctrsviridisLitewithr

Introduction to fda.vi

Rendered fromintroduction.Rmdusingknitr::rmarkdownon Jun 20 2026.

Last update: 2026-06-20
Started: 2026-06-20