wwntests: Hypothesis Tests for Functional Time Series

Provides an array of white noise hypothesis tests for functional data and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, and Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, respectively.

Version: 1.0.1
Depends: R (≥ 3.4.0)
Imports: sde, stats, ftsa, rainbow, MASS, graphics
Suggests: testthat, knitr, rmarkdown, fOptions, CompQuadForm, tensorA
Published: 2020-05-18
Author: Daniel Petoukhov [aut, cre]
Maintainer: Daniel Petoukhov <dvpetouk at uwaterloo.ca>
BugReports: https://github.com/jimthemadmanlahey/wwntests/issues
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: wwntests results


Reference manual: wwntests.pdf
Vignettes: The wwntests package
Package source: wwntests_1.0.1.tar.gz
Windows binaries: r-devel: wwntests_1.0.1.zip, r-release: wwntests_1.0.1.zip, r-oldrel: wwntests_1.0.1.zip
macOS binaries: r-release: wwntests_1.0.1.tgz, r-oldrel: wwntests_1.0.1.tgz
Old sources: wwntests archive


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