global_fit_function: Global optimization of a given function given empirical data...
In SkeweDF: Optimization of Skewed Distributions with Birth-Death Processes

This function generates a single set of optimized parameters and Psi Criterion for a given function within specified starting parameter bounds. This function uses a modified grid search method for optimization

global_fit_function(
  param_bounds,
  data,
  model_fn_name,
  iter = 1,
  weighted_rt = FALSE,
  n_cores = 1,
  clust
)

`param_bounds`	A list of sequences which indicate space where parameters should be generated and fit
`data`	Vector of observed values
`model_fn_name`	Character vector indicating name of function of theoretical model to be used. For example, for Generalized_Yule(n, rho, alpha), model_fn_name <- 'Generalied Yule'
`iter`	Integer indicating number of iterations to run grid search. Increasing iterations will increase decimal point precision of output parameters.
`weighted_rt`	Boolean used to determine if the weighted right-tail cumulative distribution function should be used or not.
`n_cores`	Integer used to indicate number of cores to be used for this function if a socket cluster object is not defined.
`clust`	socket cluster object from 'parallel::makeCluster()'. This is used if you have already generated a socket cluster object and would like to run this functoin on it. If no object is defined, one will be made for this function call.