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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.0 |
rolling linux/jammy R-4.5 | fda.vi_1.0.0.tar.gz |
395.0 KiB |
1.0.0 |
rolling linux/noble R-4.5 | fda.vi_1.0.0.tar.gz |
394.9 KiB |
1.0.0 |
rolling source/ R- | fda.vi_1.0.0.tar.gz |
496.5 KiB |
1.0.0 |
latest linux/jammy R-4.5 | fda.vi_1.0.0.tar.gz |
395.0 KiB |
1.0.0 |
latest linux/noble R-4.5 | fda.vi_1.0.0.tar.gz |
394.9 KiB |
1.0.0 |
latest source/ R- | fda.vi_1.0.0.tar.gz |
496.5 KiB |
1.0.0 |
2026-04-23 source/ R- | fda.vi_1.0.0.tar.gz |
0 B |