parameter_grid: Implement pipeline workflow over a grid of parameters.
In taylorpourtaheri/nr: Node prioritization optimizer

Given a dataframe of differential gene expression results, this function will implement the specified pipeline with each combination of parameters and return a dataframe of performance evaluation metrics.

parameter_grid(
  deg,
  target,
  grid,
  pipeline_method = NULL,
  connected_filter = NULL,
  n_cores = 1,
  ...
)

`deg`	Object of class '`dataframe`'. Differential gene expression analysis results including log fold-changes and p-values, i.e. the output of `topTable()` from limma or `results()` from DESeq2.
`target`	A string. The gene symbol associated with the causal gene.
`grid`	Object of class '`dataframe`', where each row corresponds to a combination of parameter values. i.e. the output of `expand.grid()`.
`n_cores`	Numeric. Number of cores to run on. Default value is 1.
`pipeline`	String. The workflow pipeline to be used. The value can be either 'centrality' or 'propagation', corresponding the `centrality_pipeline()` and `propagation_pipeline()` functions.
`method`	A string specifying the network analysis method for the pipeline. See ?`centrality_pipeline()` or ?`propagation_pipeline()` for more.