bootstrap_test: Create bootstrap samples to calculate p-value
In jihuilee/VCERGM:

Create bootstrap samples to calculate p-value

bootstrap_test(object, networks, attr = NULL, phicoef0 = NULL,
  phi0 = NULL, teststat, directed = FALSE, degree.spline = 3,
  interior.knot = 3, lambda.range = seq(-3, 3, by = 0.1),
  MCMC.burnin = 10000, MCMC.interval = 1000, NBoot = 1000,
  seed = 123)

`object`	A formula object of the form (network object) ~ <model terms>. Model terms take the same form of ERGM terms from R package 'ergm'.
`networks`	A list of observed networks. It should have a list() object.
`attr`	A list of vertex attributes. Default is NULL. (i.e. No attributes)
`phicoef0`	Estimates of basis coefficients under H0
`phi0`	Estimates of phi(t) under H0
`teststat`	Test statistic calculated based on observed networks
`directed`	TRUE for analyzing directed networks. FALSE for analyzing undirected networks.
`degree.spline`	Degree of splines. Default is 3 (cubic splines).
`interior.knot`	Number of interior knots to create splines. Default is 10.
`lambda.range`	Range of lambda (Tuning parameter). Default is seq(-3, 3, by = 0.1).
`MCMC.burnin`	MCMC burnin sample size. Default is 10000.
`MCMC.interval`	Interval between selected networks. Default is 1000. The first simulated network is the (MCMC.burnin + MCMC.interval). Thereafter, every (MCMC.interval)th network will be sample.
`NBoot`	Number of bootstrap samples. Default is 1000.
`seed`	Seed number used to simulate bootstrap samples. Default is 123.