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.
|Author||Daniel Petoukhov [aut, cre]|
|Maintainer||Daniel Petoukhov <email@example.com>|
|Package repository||View on CRAN|
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