convergence.check.boot: Bootstrap convergence check
In tbonne/netTS: netTS: network time series

This function will estimate the convergence of the chosen network measure using bootstrapped samples of the data.

convergence.check.boot(data, windowsize = days(30), windowshift = days(1),
  directed = FALSE, measureFun = degree, corFun = 1, boot.samples = 100,
  SRI = FALSE, probs = c(0.025, 0.975), effortFun = NULL,
  effortData = NULL, fullData = NULL)

`data`	Dataframe with relational data in the first two rows, and a time stamp in the third row. 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`	The measurment function to perform the bootstap on (should be at the node level).
`corFun`	The method used to compare observed node/dyad values with bootstrapped values: 1-Cosine similarity, 2-pearsons correlation, 3-Euclidean distance
`boot.samples`	The number of bootstrapped samples to run (Default=100)
`SRI`	Wether to use the simple ratio index (Default=FALSE)
`probs`	The quantiles of the bootrap samples to return (Default=c(0.025,0.975)).
`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.
`fullData`	This is the full dataset, if a subset dataset is being used to compare bootstrap samples to the full dataset.