fdadensity: Functional Data Analysis for Density Functions by Transformation to a Hilbert Space

An implementation of the methodology described in Petersen and Mueller (2016) <doi:10.1214/15-AOS1363> for the functional data analysis of samples of density functions. Densities are first transformed to their corresponding log quantile densities, followed by ordinary Functional Principal Components Analysis (FPCA). Transformation modes of variation yield improved interpretation of the variability in the data as compared to FPCA on the densities themselves. The standard fraction of variance explained (FVE) criterion commonly used for functional data is adapted to the transformation setting, also allowing for an alternative quantification of variability for density data through the Wasserstein metric of optimal transport.

Version: 0.1.0
Depends: R (≥ 3.3.1)
Imports: Rcpp (≥ 0.11.5), fdapace (≥ 0.3.0)
LinkingTo: Rcpp
Suggests: testthat
Published: 2017-10-16
Author: A. Petersen, P. Z. Hadjipantelis and H.G. Mueller
Maintainer: Alexander Petersen <petersen at pstat.ucsb.edu>
BugReports: https://github.com/functionaldata/tDENS/issues
License: BSD_3_clause + file LICENSE
URL: https://github.com/functionaldata/tDENS
NeedsCompilation: yes
Materials: README
In views: FunctionalData
CRAN checks: fdadensity results

Downloads:

Reference manual: fdadensity.pdf
Package source: fdadensity_0.1.0.tar.gz
Windows binaries: r-devel: fdadensity_0.1.0.zip, r-release: fdadensity_0.1.0.zip, r-oldrel: fdadensity_0.1.0.zip
OS X El Capitan binaries: r-release: fdadensity_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: fdadensity_0.1.0.tgz

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