Frequency Domain Based Analysis: Dynamic PCA

Documentation for package ‘freqdom’ version 2.0.5

freqdom-package	Frequency domain basde analysis: dynamic PCA
%c%	Convolute (filter) a multivariate time series using a time-domain filter
cov.structure	Estimate cross-covariances of two stationary multivariate time series
dpca	Compute Dynamic Principal Components and dynamic Karhunen Loeve extepansion
dpca.filters	Compute DPCA filter coefficients
dpca.KLexpansion	Dynamic KL expansion
dpca.scores	Obtain dynamic principal components scores
dpca.var	Proportion of variance explained
filter.process	Convolute (filter) a multivariate time series using a time-domain filter
fourier.inverse	Coefficients of a discrete Fourier transform
fourier.transform	Computes the Fourier transformation of a filter given as 'timedom' object
freqdom	Create an object corresponding to a frequency domain functional
freqdom.eigen	Eigendecompose a frequency domain operator at each frequency
is.freqdom	Checks if an object belongs to the class freqdom
is.timedom	Checks if an object belongs to the class timedom
rar	Simulate a multivariate autoregressive time series
rma	Moving average process
spectral.density	Compute empirical spectral density
timedom	Defines a linear filter
timedom.norms	Compute operator norms of elements of a filter
timedom.trunc	Choose lags of an object of class 'timedom'
_PACKAGE	Frequency domain basde analysis: dynamic PCA