DCCA.dccaMethod
dcca(x,y; box_start = 3, box_stop = div(length(x),10), nb_pts = 30)

Performs the DCCA analysis of x and y. The default analysis starts with a window size of 3 up to one tenth of the total length of x for statistical reasons.

returns the dcca coefficients.

DCCA.empirical_CIMethod
empirical_CI(x,y) --> points, critical_values

Extrapolates tables found in the litterature to provide a 95% confidence interval between start and stop. The confidence interval represents the null hypothesis "no correlations". Provides "ready to plot" confidence interval, by also returning the points at which the critical values are evaluted.

DCCA.log_spaceMethod
log_space(start, stop, num)

Returns a linear spacing in loglog scale. The parameter start and stop define the range and num corresponds to the number of desired points.

DCCA.rhoDCCAMethod
rhoDCCA(x,y; box_start = 3, box_stop = div(length(x),10), nb_pts = 30)

Performs the DCCA analysis of x and y. The default analysis starts with a window size of 3 up to one tenth of the total length of x for statistical reasons. returns the different window sizes used for the analysis, and the associated dcca coefficients.

DCCA.bootsrap_CIMethod
bootstrap_CI(x,y) --> points, critical_values

Provides absolute bounds for the null hypothesis "no correlations" using a bootstrap procedure, as well as the time scales they are associated to. Provides "ready to plot" confidence interval.

DCCA.complete_CIMethod
complete_CI(data_length, points)

returns the complete interpolated 95% confidence intervals for all the points in points.

DCCA.detrending1Method
detrending(x; order = 1)

Performs a linear detrending of x. You can change the order of the polynomials to a higher order for a non-linear detrending.

DCCA.interpolateMethod
interpolate(x,y) -> interpolation_function

creates and return an interpolation function working for any point.

DCCA.nearest_neighbourMethod
nearest_neighbour(value, listOfValues) --> (nn, index)

finds and returns the nearest neighbour to value in listOfValues, as well as the index of it in listOfValue.

DCCA.partitioningMethod
partitioning(x, box_size; overlap = div(box_size,2))

Partitions x into several segment of length box_size. The default behavior is that the windowed data have a 50% overlap. To change this, change the overlap parameter.

returns an array of size (n,box_size), n being the total number of segments.