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)
VDPO
The VDPO package provides tools for working with and analyzing functional data of varying lengths. This variation in length can occur in two different scenarios: Variable Domain Data and Partially Observed Data. This refers to cases where the domain over which the data is observed changes between observations or when the functional data are not fully observed over the entire domain of interest, respectively. For instance, in growth curve analysis, each individual might have measurements starting and ending at different ages, leading to varying observation ranges. Similarly, in environmental studies, different locations might have data collected over distinct time periods, creating domains of different lengths.
This publication/result/equipment/video/activity/contract/other is part of the project/grant PDC2022-133359-I00 funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU/PRTR”.
Related Papers
- Pavel Hernandez-Amaro, Maria Durban, M. Carmen Aguilera-Morillo, Cristobal Esteban Gonzalez, Inmaculada Arostegui. "Modelling physical activity profiles in COPD patients: a fully functional approach to variable domain functional regression models." doi: 10.48550/arXiv.2401.05839
Installation
To install the package from GitHub, the remotes package is required:
# install.packages("remotes")
remotes::install_github("Pavel-Hernadez-Amaro/VDPO")
Web
The web page of the package can be accessed from this link.