Package: wwntests 1.1.0

Mihyun Kim

wwntests: Hypothesis Tests for Functional Time Series

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.

Authors:Mihyun Kim [aut, cre], Daniel Petoukhov [aut]

wwntests_1.1.0.tar.gz
wwntests_1.1.0.tar.gz(r-4.7-any)wwntests_1.1.0.tar.gz(r-4.6-any)
wwntests_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
wwntests/json (API)

# Install 'wwntests' in R:

install.packages('wwntests', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/veritasmih/wwntests/issues

On CRAN:

2.30 score 2 stars 5 scripts 289 downloads 11 exports 125 dependencies

Last updated from:3361ce3f91. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	267
source / vignettes	OK	223
linux-release-x86_64	OK	233
wasm-release	OK	187

Exports:autocorrelation_coeff_plot block_bootsrap brown_motion far_1_S fgarch_1_1 fport_test GOF_far independence_test multi_lag_test single_lag_test spectral_test

Dependencies:abind ash backports base64enc bitops boot bslib cachem checkmate class cli cluster colorspace cpp11 cvar data.table deSolve digest e1071 ecp evaluate evgam farver fastICA fastmap fBasics fda fdapace fds fGarch FNN fontawesome forecast foreign Formula fracdiff fs ftsa gbutils generics geometry ggplot2 glue GPArotation gridExtra gss gtable hdrcde highr Hmisc htmlTable htmltools htmlwidgets isoband jquerylib jsonlite kernlab KernSmooth knitr ks labeling LaplacesDemon lattice lifecycle linprog Lmoments lmtest locfit lpSolve magic magrittr MASS Matrix mclust memoise mgcv mime mnormt multicool mvtnorm nlme nnet numDeriv pcaPP pdfCluster pracma proxy psych R6 rainbow rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppProgress RCurl Rdpack rlang rmarkdown ROOPSD rpart rstudioapi S7 sandwich sass scales sde spatial stabledist stringi stringr strucchange timeDate timeSeries tinytex urca vars vctrs viridisLite withr xfun yaml zoo

wwntests

Rendered fromwwntests.Rmdusingknitr::rmarkdown

Last update: 2023-12-02
Started: 2023-12-02

Help page	Topics
`autocorrelation_coeff_h` Computes the approximate functional autocorrelation coefficient at a given lag.	autocorrelation_coeff_h
Plot Confidence Bounds of Estimated Functional Autocorrelation Coefficients	autocorrelation_coeff_plot
Compute the approximate autocovariance at specified lag	autocov_approx_h
Compute weak white noise confidence bound for autocorrelation coefficient.	B_h_bound
Compute strong white noise confidence bound for autocorrelation coefficient.	B_iid_bound
Bartlett Kernel Function	bartlett_kernel
`block_bootstrap` Performs a block bootstrap on the functional data f_data with block size b.	block_bootsrap
`brown_motion` Creates at J x N matrix, containing N independent Brownian motion sample paths in each of the columns.	brown_motion
Center functional data	center
List storage of diagonal covariances.	covariance_diag_store
Compute the approximate covariance tensor for lag windows defined by i,j	covariance_i_j
Compute the approximate covariance tensor for lag windows defined by i,j	covariance_i_j_vec
Daniell Kernel Function	daniell_kernel
Compute the diagonal covariance	diagonal_autocov_approx_0
Compute the approximate diagonal covariance matrix for lag windows defined by i	diagonal_covariance_i
`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].	far_1_S
`fgarch_1_1` Simulates an fGARCH(1,1) process with N independent observations, each observed	fgarch_1_1
Compute Functional Hypothesis Tests	fport_test
Goodness-of-fit test for FAR(1)	GOF_far
Compute part of the covariance under a strong white noise assumption	iid_covariance
Compute part of the covariance under a strong white noise assumption	iid_covariance_vec
Independence Test	independence_test
Multi-Lag Hypothesis Test	multi_lag_test
Parzen Kernel Function	parzen_kernel
Compute size alpha single-lag hypothesis test under weak or strong white noise assumption	Q_WS_hyp_test
Compute the approximate covariance at a point for lag windows defined by i,j	scalar_covariance_i_j
Compute the approximate covariance at a point for lag windows defined by i,j	scalar_covariance_i_j_vec
Single-Lag Hypothesis Test	single_lag_test
Spectral Density Test	spectral_test

Package: wwntests 1.1.0

wwntests: Hypothesis Tests for Functional Time Series

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