Package index • PaleoSpec

All functions

AddConfInterval(): Add confidence interval

AnPowerlaw(): A PSD(freq) for a powerlaw with variance 1

ApplyFilter(): Filter time series

ApproxNearest(): approximate a timeseries using the nearest neighbour

AvgToBin(): Bin averaging

Bandpass(): Calculate Weights for a Bandpass Filter

BinTimeseries(): Bin a Timeseries Preserving Empty Bins

ClosestElement(): Get closest element of a vector

ColTransparent(): Modify a color to get brighter and tranparent for the confidence intervals

ConfRatio(): Confidence Interval of ratios

ConfVar(): Provide ChiSquared confidence intervals for ratios

DF2Spec(): Transform a spec_df Object into a spec Object or List of spec Objects

FilterSpec(): Filter a Power Spectrum Object

FilterSpecLog(): Smooth a Spectrum with Evenly Spaced Bins in Logspace

FirstElement(): first element of a vector

GetTransferFunction(): Derives and plots the transfer function (given a filter)

GetVarFromSpectra(): Variance estimate by integrating a part of the spectrum

Highpass(): calculate weights for a bandpass filter

InverseFilter(): Construct the inverse filter in the time domain

LLines(): Add a spectrum to an existing log-log spectral plot.

LPlot(): Log-log spectral plot.

LastElement(): last element of a vector

LogSmooth(): Smooths the spectrum using a log smoother

Lowpass(): Calculate weights for lowpass filter

MakeEquidistant(): Average an irregular timeseries to a regular timeseries

MeanSpectrum(): Weighted mean spectrum

MonthlyFromDaily(): Bin daily values to monthly values

NaFillTs(): NaFill

PS.VarUntilF(): Variance of a powerlaw process if integrated from until frequency f

PSP.CorAfterRollmean(): Numerical correlation of random timeseries with different filtering (running mean)

PSP.CorUntilF(): lowpass filtered expected correlation of powerlaw signal pair

SimFromEmpiricalSpec(): Simulate a random timeseries consistent with an arbitrary numerical power spectrum

SimPLS(): Simulate a random timeseries with a powerlaw spectrum

SimPowerlaw(): Simulate a random timeseries with a powerlaw spectrum

SimPowerlawPiecewise(): Simulate a timeseries with length N which has a spectra consisting of two powerlaws

SimProxySeries() sim.proxy.series(): Simulate a Proxy Time Series Assuming a Power-Law Power Spectrum of the Climate

SimulatePowerlawSignalPair(): Create a pair of random signals with powerlaw signal and powerlaw noise

SlopeFit(): Fit a power-law to the spectrum

Spec2DF(): Transform Spec Object(s) Into a Dataframe

SpecACF(): Estimate Power Spectra via the Autocovariance Function With Optional Slepian Tapers

SpecInterpolate(): Interpolate spectrum

SpecMTM(): MTM spectral estimator

SubsampleTimeseriesBlock(): Subsample (downsample) timeseries using block averaging#'

TrimNA(): Remove leading and trailing rows of all NA

as.data.frame(<spec>): Transform a spec Object Into a Dataframe

as.spec(): Make a spec Object

as_spec_df(): Make a spec_df Object

gg_spec(): Plot One or More Spectra with ggplot2

is.spectrum(): Check for spectral object

remove.highestFreq(): Remove high frequencies

remove.lowestFreq(): Remove low frequencies