The autocorrelation plugin calculates correlation values between a series (vector) and a lagged version of itself, using lag values from floor(-(N-1)/2) to floor((N-1)/2), where N is the number of points in the data set. The time vector is not an input as it is assumed that the data is sampled at equal time intervals. The correlation value r at lag k is:

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The crosscorrelation plugin calculates correlation values between two series (vectors) x and y, using lag values from floor(-(N-1)/2) to floor((N-1)/2), where N is the number of elements in the longer vector. The shorter vector is padded to the length of the longer vector using 0s. The time vector is not an input as it is assumed that the data is sampled at equal time intervals. The correlation value r at lag k is:

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The cross power spectrum plugin takes two vectors and calculates the FFT of their cross-correlation values.

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The convolution plugin generates the convolution of one vector with another. The convolution of two functions f and g is given by:

The order of the vectors does not matter, since f*g=g*f. In addition, the vectors do not need to be of the same size, as the plugin will automatically interpolate smallest vector to the larger length.

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The deconvolution plugin generates the deconvolution of one vector with another. Deconvolution is the inverse of convolution. Given the convolved vector h and another vector g, the deconvolution f is given by:

The vectors do not need to be of the same size, as the plugin will automatically extrapolate the shorter vector. The shorter vector is assumed to be the response function g.

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The periodogram plugin produces the periodogram of a given data set. Periodogram is an estimate of the spectral density of a signal.

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