wild_cluster_boot: Calculate wild cluster bootstrap
In mattdwebb/wildclusterboot: Wild Cluster Bootstrap

This function provides a flexible framework for calculating the wild cluster bootsrap, this function implements the original wild cluster boostrap, due to Cameron, Gelbach and Miller (2008), and a number of modifications, including flexible boostrap distributions and implementing a "multi-way" version.

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wild_cluster_boot(data, model, x_interest, clusterby, boot_dist, boot_reps,
  bootby = clusterby, H0 = 0, enum = FALSE, absval = FALSE,
  bound = c("upper", "lower", "mid", "uniform", "density"))

`data`	Dataframe with all data, including group indices
`model`	lm object to perform the boostrap with
`x_interest`	String indicating the name of the paramater of interest
`clusterby`	String or formula with name of clusterby variable in data. Use a string for one-way clustering and a formula to indicate multiway clustering (e.g. use ~ G + H to a clustering using the G and H dimensions
`boot_dist`	Specifies the distribution from which to draw the wild boostrap weights. This can be one of three things: a numeric vector, a string specifying default distribution ("rad" or "six" for now)vor a function that takes only the argument "n" (e.g. rnorm)
`boot_reps`	Integer indicating number of bootstrap repetitions
`bootby`	String with name of bootby variable. The default for this is same as clusterby, however a single dimension must be specified if the clusterby variable is a multi-way formula
`H0`	Float or integer inticating the null hypothesis, default is 0
`enum`	Boolean indicating whether to calculate all possible wild bootstrap combinations. Only valid if using a vector or default distribution. If this is set to TRUE, then the boot_reps variable is ignored and a new enumerated total is calculated
`absval`	Boolean indicating whether or not to use absolute valued t-statistics
`bound`	String or vector of string indicating which bootstrap "bound" to use when tie-breaking. upper and lower indicate using the highest or lowest value when actual value is tied with bootstrapped values. mid takes the half-way point between the two. uniform calcualtes a random interval betwen the two and density uses a kernel smoothing correction due to Racine-MacKinnon (2007)