Basic Examples (2) 

Calculate the overlapping modified Allan deviation of white noise at a rate of 0.1 for automatically chosen sampling points:

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Plot the results:

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Display the overlapping modified Allan deviation of white noise at a rate of 0.1 for certain sampling points:

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Scope (3) 

Sample at all possible times:

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Try to sample at five different time values:

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The function will try to use a fast evaluation method when the data array is fully real, and otherwise prints a warning:

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Options (2) 

FrequencyData (1) 

"FrequencyData" accepts a Boolean as argument. True signals the use of frequency data as opposed to phase data:

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Overlapping (1) 

"Overlapping" accepts a Boolean as argument. False signals the use of non-overlapping strides in the deviation sampling:

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Applications (2) 

Distinguish a random walk from white noise by their distinct slopes:

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Detect a drift in frequency data by a rising slope in longer sample times:

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Properties and Relations (3) 

Using the fast algorithm is approximately two times faster:

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The function can process Around objects as well as exact inputs directly:

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Compare ModifiedAllanDeviation with the AllanDeviation resource function:

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Possible Issues (1) 

Requesting a certain amount of samples will not necessarily result in this amount of deviations:

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Neat Examples (1) 

The function can distinguish between white phase noise and flicker noise (pink noise), as opposed to the plain Allan deviation:

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