pairwise_cmp: Wrapper to compute CMP for each combination of a given pair...
In UCIDataLab/assocr: Quantify degreee of assocation between discrete event series

Wrapper to compute CMP for each combination of a given pair of user data

1 2	pairwise_cmp(data, n, W = c(0, 7), bidirectional = TRUE, samp = "empirical", sampSpace = NULL, rng = NULL, mark = 1)

`data`	List of data.frames for two pairs of event series (output of `assocr::sessionize_data()`): `data`: sessionized data, `<id, m, sid, t>` `sessions`: summary of sessionized data, `<id, m, sid, n, t>`
`n`	Number of samples to draw
`W`	Vector containing observation window `c(low, high)`.
`bidirectional`	Logical indicating if the inter-event times should be bidirectional (versus only looking at events prior to event of interest)
`samp`	Sampling technique to use. One of `empirical`: sessionized resampling from empirical distn of start times `gaussian`: sessionized resampling from Gaussian distn such that 99 `uniform`: sessionized resampling from Uniform distn over `rng`
`sampSpace`	vector representation of empirical sample space for session start times if `samp == "empirical"`
`rng`	Data.frame of ranges for resampling; column names correspond to ids and the 2 rows correspond to (lower, upper) limits
`mark`	which mark to perform the seampling for; `default == 1`