wwntests

Provides a collection of white noise hypothesis tests for functional time series 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. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. 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, Gabrys and Kokoszka (2007) doi:10.1198/016214507000001111, and Kim et al. (2023) doi:10.1214/23-SS143 respectively.

Last 30 days

Last 365 days

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.