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wwntests (version 1.1.0)

Hypothesis Tests for Functional Time Series

Description

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) , Characiejus and Rice (2019) , Gabrys and Kokoszka (2007) , and Kim et al. (2023) respectively.

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Install

install.packages('wwntests')

Monthly Downloads

95

Version

1.1.0

License

GPL-3

Maintainer

Mihyun Kim

Last Published

December 1st, 2023

Functions in wwntests (1.1.0)

iid_covariance

Compute part of the covariance under a strong white noise assumption
covariance_i_j_vec

Compute the approximate covariance tensor for lag windows defined by i,j
iid_covariance_vec

Compute part of the covariance under a strong white noise assumption
independence_test

Independence Test
multi_lag_test

Multi-Lag Hypothesis Test
spectral_test

Spectral Density Test
parzen_kernel

Parzen Kernel Function
scalar_covariance_i_j

Compute the approximate covariance at a point for lag windows defined by i,j
scalar_covariance_i_j_vec

Compute the approximate covariance at a point for lag windows defined by i,j
single_lag_test

Single-Lag Hypothesis Test
GOF_far

Goodness-of-fit test for FAR(1)
B_iid_bound

Compute strong white noise confidence bound for autocorrelation coefficient.
Q_WS_hyp_test

Compute size alpha single-lag hypothesis test under weak or strong white noise assumption
autocov_approx_h

Compute the approximate autocovariance at specified lag
block_bootsrap

`block_bootstrap` Performs a block bootstrap on the functional data f_data with block size b.
B_h_bound

Compute weak white noise confidence bound for autocorrelation coefficient.
autocorrelation_coeff_h

`autocorrelation_coeff_h` Computes the approximate functional autocorrelation coefficient at a given lag.
bartlett_kernel

Bartlett Kernel Function
brown_motion

`brown_motion` Creates at J x N matrix, containing N independent Brownian motion sample paths in each of the columns.
autocorrelation_coeff_plot

Plot Confidence Bounds of Estimated Functional Autocorrelation Coefficients
diagonal_covariance_i

Compute the approximate diagonal covariance matrix for lag windows defined by i
covariance_i_j

Compute the approximate covariance tensor for lag windows defined by i,j
center

Center functional data
far_1_S

`far_1_S` Simulates an FAR(1,S)-fGARCH(1,1) process with N independent observations, each observed discretely at J points on the interval [0,1].
covariance_diag_store

List storage of diagonal covariances.
daniell_kernel

Daniell Kernel Function
diagonal_autocov_approx_0

Compute the diagonal covariance
fport_test

Compute Functional Hypothesis Tests
fgarch_1_1

`fgarch_1_1` Simulates an fGARCH(1,1) process with N independent observations, each observed