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: |
1.0.2 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
sde, stats, ftsa, rainbow, MASS, graphics |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, CompQuadForm, tensorA |
| Published: |
2022-11-01 |
| Author: |
Mihyun Kim [aut, cre],
Daniel Petoukhov [aut] |
| Maintainer: |
Mihyun Kim <mihyun.kim at mail.wvu.edu> |
| BugReports: |
https://github.com/veritasmih/wwntests/issues |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
NEWS |
| CRAN checks: |
wwntests results |
Documentation:
Downloads:
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