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.

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.

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.

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.

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.

complete_CI(data_length, points)

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

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.

interpolate(x,y) -> interpolation_function

creates and return an interpolation function working for any point.

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.

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.