tsCrossDistMat: Compute cross-distance matrix
In Jakob-Bach/FastTSDistances: Fast dissimilarity computations for time series

Description Usage Arguments Value

Computes the cross-distance matrix between all (time series) objects from a list, assuming a symmetric dissimilarity and a self-distance of 0. Uses parallelization.

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tsCrossDistMat(tsList, distMethod, distArgs = list(), distExports = NULL,
  distPackages = NULL, cpuCores = 2, maxSequentialTime = 3,
  trace = TRUE)

`tsList`	A list containing the objects (e.g. numeric vectors representing time series).
`distMethod`	The name of a function which should be used to calculate the distance between two objects.
`distArgs`	Further parameters which should be passed to the distance function in the form of a named list.
`distExports`	Function (not part of a package) which are need for the distance computation and therefore need to be exported to work with `foreach`.
`distPackages`	Packages which are necessary for using the `distMethod`.
`cpuCores`	The number of cores/threads which should be used for parallelization. Setting this value to 1 forces sequential computation.
`maxSequentialTime`	A short speed test is performed to decide if parallelization is worth it (as it comes with a certain overhead). If the speed test expects a total runtime <= the parameter value (in seconds), it calls the distance method sequentially. A parameter value <= 0 forces parallel execution (without speed test).
`trace`	Print status information? (currently only the computation time after finishing the computation).