removes the mean
returns STD(X)/MEAN(X)
2-dimensional convolution X and Y can contain missing values encoded with NaN.
calculates the correlation matrix X and Y can contain missing values encoded with NaN.
calculates the correlation coefficient.
covariance matrix X and Y can contain missing values encoded with NaN.
generates covariance matrix X and Y can contain missing values encoded with NaN.
removes the trend from data, NaN's are considered as missing values
The use of FLAG_IMPLICIT_SIGNIFICANCE is in experimental state.
Compatibility function --- same as mean(x,'g')
Compatibility function --- same as mean(x,"h")
estimates the kurtosis
mean absolute deviation of X
calculates the mean of data elements.
estimates the Mean deviation
calculates the mean of the squares
data elements,
calculates Modules Y from X
estimates the p-th moment
checks whether the functions from NaN-toolbox have been correctly installed.
nanstd is identical to the std function except that NaN values are ignored.
nansum is identical to the sum function except that NaN values are treated as 0 and so ignored.
checks several mathematical operations and a few statistical functions for their correctness related to NaN's.
returns normal cumulative distribtion function
returns inverse cumulative function of the normal distribution
returns normal probability density
calculates the percentiles of histograms and sample arrays.
calculates the quantiles of histograms and sample arrays.
calculated the rank correlation coefficient.
gives the rank of each element in a vector.
calculates remainder of X / Y
calculates the root mean square can deal with complex data.
calculates the standard error of the mean
estimates the skewness
Spearman's rank correlation coefficient.
estimates various statistics at once.
calculates the standard deviation.
adds all non-NaN values.
adds all non-NaN values.
returns student cumulative distribtion function
returns inverse cumulative function of the student distribution
returns student probability density
evaluates basic statistics of a data series m = TRIMEAN(y).
calculates the variance.
generates cross-covariance function.
compute the z-score of each element of X relative to the data in the columns of X.
