Package: ftsspec 1.0.1

ftsspec: Spectral Density Estimation and Comparison for Functional Time Series

Functions for estimating spectral density operator of functional time series (FTS) and comparing the spectral density operator of two functional time series, in a way that allows detection of differences of the spectral density operator in frequencies and along the curve length. A description of the implemented tests can be found in the paper Tavakoli & Panaretos (2016) <doi:10.1080/01621459.2016.1147355>.

Authors:Shahin Tavakoli [aut, cre]

ftsspec_1.0.1.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ftsspec/json (API)

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

Bug tracker:https://github.com/stavakol/ftsspec/issues

On CRAN:

Conda:

2.70 score 7 scripts 304 downloads 10 exports 16 dependencies

Last updated from:bd27744f62. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK94
source / vignettesOK155
linux-releaseOK102
macos-releaseOK127
macos-oldrelOK194
windows-develOK55
windows-releaseOK59
windows-oldrelOK61
wasm-releaseOK82

Exports:Epanechnikov_kernelGenerate_filterMAMarginal_basis_pvalPvalAdjustSimulate_new_MASpecSpec_compare_fixed_freqSpec_compare_localize_freqSpec_compare_localize_freq_curvelengthSpecMA

Dependencies:clicodagluelatticelifecyclemagrittrMatrixnetworkpillarpkgconfigrlangsnastatnet.commontibbleutf8vctrs

Readme and manuals

Help Manual

Help pageTopics
The Epanechnikov weight function, with support in [-1,1]Epanechnikov_kernel
ftsspec: collection of functions for estimating spectral density operator of functional time series (FTS) and comparing the spectral density operator of two functional time series, in a way that allows detection of differences of the spectral density operator in frequencies and along the curve length.ftsspec-package ftsspec
Generate the Filter of a multivariate MA processGenerate_filterMA
Get the square root of the covariance matrix associated to a noise typeGet_noise_sd
Plotting function for 'SampleSpecDiffFreq' classlines.SampleSpecDiffFreq
Compute the marginal p-values at each basis coefficients of for testing the equality of two spectral density kernelsMarginal_basis_pval
Plotting method for object inheriting from class 'SampleSpec'plot.SampleSpec
Plotting function for 'SampleSpecDiffFreq' classplot.SampleSpecDiffFreq
Plotting method for class 'SampleSpecDiffFreqCurvelength'plot.SampleSpecDiffFreqCurvelength
Plotting method for object inheriting from class 'SpecMA'plot.SpecMA
Printing method for class 'SampleSpecDiffFreqCurvelength'print.SampleSpecDiffFreqCurvelength
Generic function to adjust pvaluesPvalAdjust PvalAdjust.SampleSpecDiffFreq
Simulate a new Moving Average (MA) vector time series and return the time seriesSimulate_new_MA
Compute Spectral Density of Functional Time SeriesSpec
Test if two spectral density operators at some fixed frequency are equal.Spec_compare_fixed_freq
Compare the spectral density operator of two Functional Time Series and localize frequencies at which they differ.Spec_compare_localize_freq
Compare the spectral density operator of two Functional Time Series and localize frequencies at which they differ, and (spatial) regions where they differSpec_compare_localize_freq_curvelength
'Spectral density operator of a MA vector process' ObjectSpecMA