Hypothesis Tests for Functional Time Series

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Documentation for package ‘wwntests’ version 1.1.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

autocorrelation_coeff_h	'autocorrelation_coeff_h' Computes the approximate functional autocorrelation coefficient at a given lag.
autocorrelation_coeff_plot	Plot Confidence Bounds of Estimated Functional Autocorrelation Coefficients
autocov_approx_h	Compute the approximate autocovariance at specified lag
bartlett_kernel	Bartlett Kernel Function
block_bootsrap	'block_bootstrap' Performs a block bootstrap on the functional data f_data with block size b.
brown_motion	'brown_motion' Creates at J x N matrix, containing N independent Brownian motion sample paths in each of the columns.
B_h_bound	Compute weak white noise confidence bound for autocorrelation coefficient.
B_iid_bound	Compute strong white noise confidence bound for autocorrelation coefficient.
center	Center functional data
covariance_diag_store	List storage of diagonal covariances.
covariance_i_j	Compute the approximate covariance tensor for lag windows defined by i,j
covariance_i_j_vec	Compute the approximate covariance tensor for lag windows defined by i,j
daniell_kernel	Daniell Kernel Function
diagonal_autocov_approx_0	Compute the diagonal covariance
diagonal_covariance_i	Compute the approximate diagonal covariance matrix for lag windows defined by i
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].
fgarch_1_1	'fgarch_1_1' Simulates an fGARCH(1,1) process with N independent observations, each observed
fport_test	Compute Functional Hypothesis Tests
GOF_far	Goodness-of-fit test for FAR(1)
iid_covariance	Compute part of the covariance under a strong white noise assumption
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
parzen_kernel	Parzen Kernel Function
Q_WS_hyp_test	Compute size alpha single-lag hypothesis test under weak or strong white noise assumption
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
spectral_test	Spectral Density Test