Learn R Programming

spectral (version 2.0)

Common Methods of Spectral Data Analysis

Description

On discrete data spectral analysis is performed by Fourier and Hilbert transforms as well as with model based analysis called Lomb-Scargle method. Fragmented and irregularly spaced data can be processed in almost all methods. Both, FFT as well as LOMB methods take multivariate data and return standardized PSD. For didactic reasons an analytical approach for deconvolution of noise spectra and sampling function is provided. A user friendly interface helps to interpret the results.

Copy Link

Version

Install

install.packages('spectral')

Monthly Downloads

292

Version

2.0

License

GPL-2

Last Published

March 29th, 2021

Functions in spectral (2.0)

gLmb

generalized Lomb-Scargle estimation function
H

The Hilbert transformation
amax

Local Maxima
analyticFunction

Analytic function
envelope

Calculates the envelope of a band limited signal
filter.lomb

Filter and reconstruction of data analysed via spec.lomb
Windowfunctions

Windowfunctions
BP

Simple bandpass function
filter.fft

Filter in the frequency domain
deconvolve

Deconvolve Sampling Spectrum for Equidistant Sampling
.onAttach

Setting up multithread BLAS library
plot.lomb

plot method for Lomb-Scargle periodograms
plot.fft

Plot fft-objects
.onDetach

Reset multithread BLAS to default
win.cos

Cosine window function
waterfall

Estimate the local frequencies
lmb

Lomb-Scargle estimation function
interpolate.fft

interpolates data using the Fourier back transform
win.tukey

Tukey window function
print.lomb

Lomb-Plotting Function
win.nutt

Nuttall window function
print.fft

FFT-Plotting Function
win.hann

Hanning window function
spec.fft

1D/2D/nD (multivariate) spectrum of the Fourier transform
spec.lomb

Lomb-Scargle Periodigram
summary.fft

Summarize FFT objects
summary.lomb

Summarize Lomb objects