fdaPOIFD - Partially Observed Integrated Functional Depth
Applications to visualization, outlier detection and classification. Software companion for Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M., (2022), "Integrated Depth for Partially Observed Functional Data". Journal of Computational and Graphical Statistics. \url{https://doi.org/10.1080/10618600.2022.2070171}.
Last updated 3 years ago
4.00 score 2 stars 2 scripts 183 downloadslocalFDA - Localization Processes for Functional Data Analysis
Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.
Last updated 4 years ago
classificationfunctional-data-analysisimputationoutliers-detection
2.70 score 96 downloads