Publisher review:RUNMEAN - Very fast running mean (aka moving average) filter For vectors, Y = RUNMEAN(X,M) computes a running mean (also known as moving average) on the elements of the vector X. It uses a window of 2*M 1 datapoints. M an positive integer defining (half) the size of the window. In pseudo code:Y(i) = sum(X(j)) / (2*M 1), for j = (i-M):(i M), and i=1:length(X) For matrices, Y = RUNMEAN(X,M) or RUNMEAN(X,M,[]) operates on the first non-singleton dimension of X. RUNMEAN(X,M,DIM) computes the running mean along the dimension DIM.If the total window size (2*M 1) is larger than the size in dimension DIM, the overall average along dimension DIM is computed.As always with filtering, the values of Y can be inaccurate at the edges. RUNMEAN(..., MODESTR) determines how the edges are treated. MODESTR can be one of the following strings:'edge' : X is padded with first and last values along dimension DIM (default)'zero' : X is padded with zeros'mean' : X is padded with the mean along dimension DIM X should not contains NaNs, yielding an all NaN result. NaNs can be replaced by using, e.g., "inpaint_nans" created by John D'Errico.This is an incredibly fast implementation of a running mean, since execution time does not depend on the size of the window. Requirements: ยท MATLAB Release: R13
RUNMEAN 3.0 is a Matlab script for Signal Processing scripts design by Josh.
It runs on following operating system: Windows / Linux / Mac OS / BSD / Solaris.
Operating system:Windows / Linux / Mac OS / BSD / Solaris
RUNMEAN 3.0 script details 
