permutation.graph.values: Use permutation to extract uncertainty
In tbonne/netTS: netTS: network time series

This function will estimate network measures given random permutations on the original data.

permutation.graph.values(data, windowsize, windowshift, directed = FALSE,
  measureFun, effortFun = NULL, effortData = NULL, probs = 0.95,
  nperm = 1000, SRI = FALSE, graphlist = NULL,
  permutationFun = perm.events)

`data`	A dataframe with relational data in the first two columns, and a time stamp in the third column. An optional column with a weight can be added if there is a duration or magnitude for each interaction (column name for this should be set to 'weight'). Note: time stamps should be in ymd or ymd_hms format. The lubridate package can be very helpful in organizing times.
`windowsize`	The size of each window in which to generate a network.
`windowshift`	The amount of time to shift the window when generating networks.
`directed`	Whether to consider the network as directed or not (TRUE/FALSE).
`measureFun`	This is a function that takes as an input a igraph network and returns a single value.
`effortFun`	This is a function that takes as input the data within a window of time and returns the total sampling effort.
`effortData`	This is a dataframe containing the data used to calculate sampling effort. The first column should contain timedate values.
`probs`	numeric vector of probabilities with values in [0,1].
`nperm`	Number of permutations to perform before extracting network measures.
`SRI`	Wether to use the simple ratio index (Default=FALSE).
`graphlist`	A list of networks to perfrom permutations on.
`permutationFun`	A function that will be used to perform permutations on the event data (i.e., before the network) or on the network itself. See vignette: Using_permutations.