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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>.
Package details |
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| Author | Mihyun Kim [aut, cre], Chi-Kuang Yeh [aut] (<https://orcid.org/0000-0001-7057-2096>), Yuqian Zhao [aut], Gregory Rice [ctb] |
| Maintainer | Mihyun Kim <mihyun.kim@mail.wvu.edu> |
| License | GPL-3 |
| Version | 1.0.0 |
| URL | https://github.com/veritasmih/FTSgof |
| Package repository | View on CRAN |
| Installation |
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