Package: VDPO 0.2.0

VDPO: Working with and Analyzing Functional Data of Varying Lengths

Comprehensive set of tools for analyzing and manipulating functional data with non-uniform lengths. This package addresses two common scenarios in functional data analysis: Variable Domain Data, where the observation domain differs across samples, and Partially Observed Data, where observations are incomplete over the domain of interest. 'VDPO' enhances the flexibility and applicability of functional data analysis in 'R'. See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>, Hernandez-Amaro et al. (2025) <doi:10.48550/arXiv.2510.26917>, and Hernandez-Amaro et al. (2026) <doi:10.48550/arXiv.2605.03633>.

Authors:Pavel Hernandez [aut, cre], Jose Ignacio Diez [ctr], Maria Durban [ctb], Maria del Carmen Aguilera-Morillo [ctb]

VDPO_0.2.0.tar.gz
VDPO_0.2.0.zip(r-4.7)VDPO_0.2.0.zip(r-4.6)VDPO_0.2.0.zip(r-4.5)
VDPO_0.2.0.tgz(r-4.6-any)VDPO_0.2.0.tgz(r-4.5-any)
VDPO_0.2.0.tar.gz(r-4.7-any)VDPO_0.2.0.tar.gz(r-4.6-any)
VDPO_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
VDPO/json (API)

# Install 'VDPO' in R:
install.packages('VDPO', repos = c('https://pavel-hernadez-amaro.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/pavel-hernadez-amaro/vdpo/issues

Pkgdown/docs site:https://pavel-hernadez-amaro.github.io

On CRAN:

Conda:

5.26 score 18 scripts 215 downloads 13 exports 45 dependencies

Last updated from:753d6a3772. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK177
source / vignettesOK194
linux-release-x86_64OK203
macos-release-arm64OK131
macos-oldrel-arm64OK137
windows-develOK143
windows-releaseOK141
windows-oldrelOK152
wasm-releaseOK138

Exports:add_gridadjust_proportiondata_generator_po_1ddata_generator_po_2ddata_generator_vdffpoffpo_2dffvdmfpca_vdplot_cipo_2d_fitpo_fitvd_fit

Dependencies:ashbitopscliclustercolorspacecpp11deSolvefarverfdafdsFNNggplot2gluegtablehdrcdeisobandkernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmulticoolmvtnormnlmepcaPPpracmapROCR6rainbowRColorBrewerRcppRCurlrlangS7scalesSOPvctrsviridisLitewithr

Introduction to VDPO
Simulation Studies | Data Generation Function | Simulation Parameters | Basic Parameters | Domain Generation | Functional Data Generation | Response Generation | Example Usage | Output Structure | Notes | Visualizing Simulated Data | Multiple Functional Curves | Original vs Noisy Curve
Rendered fromVDPO-01-introduction.Rmdusingknitr::rmarkdown

Last update: 2026-06-06
Started: 2024-10-09

Model fitting for partially observed functional data
Introduction | Data generation | Model fitting | Estimated coefficient | The two-dimensional case | References
Rendered fromVDPO-03-pofd.Rmdusingknitr::rmarkdown

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

Multivariate functional principal component analysis for variable domain data
Introduction | Data generation | Specifying the observation times | Estimation | Eigenfunctions | Scores | References
Rendered fromVDPO-04-vd-mfpca.Rmdusingknitr::rmarkdown

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

Model fitting for variable domain functional data
Introduction | Data Generation | Model Fitting | Basic Model with Single Functional Covariate | Model with Multiple Functional Covariates | Model with Functional and Non-Functional Covariates | Model Summary | Working with Non-Aligned Data | Additional functionality | Plotting the betas | Final remarks
Rendered fromVDPO-02-vd-models.Rmdusingknitr::rmarkdown

Last update: 2026-06-06
Started: 2024-10-16