FTSgof: White Noise and Goodness-of-Fit Tests for Functional Time Series
It offers comprehensive tools for the analysis of functional
time series data, focusing on white noise hypothesis testing and
goodness-of-fit evaluations, alongside functions for
simulating data and advanced visualization techniques, such as 3D
rainbow plots. These methods are described in Kokoszka, Rice, and Shang (2017) <doi:10.1016/j.jmva.2017.08.004>,
Yeh, Rice, and Dubin (2023) <doi:10.1214/23-EJS2112>, Kim, Kokoszka, and Rice (2023) <doi:10.1214/23-ss143>, and
Rice, Wirjanto, and Zhao (2020) <doi:10.1111/jtsa.12532>.
Version: |
1.0.0 |
Depends: |
R (≥ 3.5.0) |
Imports: |
sde, graphics, stats, rgl, fda, nloptr, sfsmisc, MASS |
Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: |
2024-10-03 |
DOI: |
10.32614/CRAN.package.FTSgof |
Author: |
Mihyun Kim [aut, cre],
Chi-Kuang Yeh
[aut],
Yuqian Zhao [aut],
Gregory Rice [ctb] |
Maintainer: |
Mihyun Kim <mihyun.kim at mail.wvu.edu> |
BugReports: |
https://github.com/veritasmih/FTSgof/issues |
License: |
GPL-3 |
URL: |
https://github.com/veritasmih/FTSgof |
NeedsCompilation: |
no |
SystemRequirements: |
XQuartz (https://www.xquartz.org/) |
Language: |
en-US |
Materials: |
README |
CRAN checks: |
FTSgof results |
Documentation:
Downloads:
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