`DCCA.dcca`

— Method`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_CI`

— Method`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_space`

— Method`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.rhoDCCA`

— Method`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_CI`

— Method`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_CI`

— Method`complete_CI(data_length, points)`

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

.

`DCCA.detrending1`

— Method`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.interpolate`

— Method`interpolate(x,y) -> interpolation_function`

creates and return an interpolation function working for any point.

`DCCA.nearest_neighbour`

— Method`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.partitioning`

— Method`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.