fda.vi-package: fda.vi: Functional Data Analysis using Variational Inference

In fda.vi: Functional Data Analysis using Variational Inference

fda.vi: Functional Data Analysis using Variational Inference

Description

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.' \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.20758")}.

Author(s)

Maintainer: Camila de Souza [e-mail]

Authors:

• Stephen Kinsey

• Ana Carolina da Cruz

• Pedro Henrique Toledo Oliveira Sousa

See Also

Useful links:

• https://github.com/desouzalab/fda.vi

• Report bugs at https://github.com/desouzalab/fda.vi/issues

fda.vi documentation built on June 20, 2026, 5:06 p.m.