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

AuthorMihyun Kim [aut, cre], Chi-Kuang Yeh [aut] (<https://orcid.org/0000-0001-7057-2096>), Yuqian Zhao [aut], Gregory Rice [ctb]
MaintainerMihyun Kim <mihyun.kim@mail.wvu.edu>
LicenseGPL-3
Version1.0.0
URL https://github.com/veritasmih/FTSgof
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("FTSgof")

Try the FTSgof package in your browser

Any scripts or data that you put into this service are public.

FTSgof documentation built on Oct. 4, 2024, 1:06 a.m.