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-4.5-noble)wwntests_1.1.0.tar.gz(r-4.4-noble)
wwntests_1.1.0.tgz(r-4.4-emscripten)wwntests_1.1.0.tgz(r-4.3-emscripten)
wwntests.pdf |wwntests.html✨
wwntests/json (API)
NEWS

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

Peer review:

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

11 exports 2 stars 0.23 score 136 dependencies 3 scripts 187 downloads

Last updated 10 months agofrom:3361ce3f91. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 28 2024
R-4.5-linuxOKAug 28 2024

Exports:autocorrelation_coeff_plotblock_bootsrapbrown_motionfar_1_Sfgarch_1_1fport_testGOF_farindependence_testmulti_lag_testsingle_lag_testspectral_test

Dependencies:abindashbackportsbase64encbitopsbootbslibcachemcheckmateclasscliclustercolorspacecurlcvardata.tabledeSolvedigeste1071ecpevaluateevgamfansifarverfastICAfastmapfBasicsfdafdapacefdsfGarchFNNfontawesomeforecastforeignFormulafracdifffsftsagbutilsgenericsgeometryggplot2glueGPArotationgridExtragssgtablehdrcdehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonlitekernlabKernSmoothknitrkslabelingLaplacesDemonlatticelifecyclelinprogLmomentslmtestlocfitlpSolvemagicmagrittrMASSMatrixmclustmemoisemgcvmimemnormtmulticoolmunsellmvtnormnlmennetnumDerivpcaPPpdfClusterpillarpkgconfigpracmaproxypsychquadprogquantmodR6rainbowrappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressRCurlRdpackrlangrmarkdownROOPSDrpartrstudioapisandwichsassscalessdespatialstablediststringistringrstrucchangetibbletimeDatetimeSeriestinytextseriesTTRurcautf8varsvctrsviridisviridisLitewithrxfunxtsyamlzoo

wwntests

Rendered fromwwntests.Rmdusingknitr::rmarkdownon Aug 28 2024.

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

Readme and manuals

Help Manual

Help pageTopics
`autocorrelation_coeff_h` Computes the approximate functional autocorrelation coefficient at a given lag.autocorrelation_coeff_h
Plot Confidence Bounds of Estimated Functional Autocorrelation Coefficientsautocorrelation_coeff_plot
Compute the approximate autocovariance at specified lagautocov_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 Functionbartlett_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 datacenter
List storage of diagonal covariances.covariance_diag_store
Compute the approximate covariance tensor for lag windows defined by i,jcovariance_i_j
Compute the approximate covariance tensor for lag windows defined by i,jcovariance_i_j_vec
Daniell Kernel Functiondaniell_kernel
Compute the diagonal covariancediagonal_autocov_approx_0
Compute the approximate diagonal covariance matrix for lag windows defined by idiagonal_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 observedfgarch_1_1
Compute Functional Hypothesis Testsfport_test
Goodness-of-fit test for FAR(1)GOF_far
Compute part of the covariance under a strong white noise assumptioniid_covariance
Compute part of the covariance under a strong white noise assumptioniid_covariance_vec
Independence Testindependence_test
Multi-Lag Hypothesis Testmulti_lag_test
Parzen Kernel Functionparzen_kernel
Compute size alpha single-lag hypothesis test under weak or strong white noise assumptionQ_WS_hyp_test
Compute the approximate covariance at a point for lag windows defined by i,jscalar_covariance_i_j
Compute the approximate covariance at a point for lag windows defined by i,jscalar_covariance_i_j_vec
Single-Lag Hypothesis Testsingle_lag_test
Spectral Density Testspectral_test