clustering: (Internal function) Perform the clustering process of ipcaps
In kridsadakorn/ipcaps.bioc: Iterative Pruning to Capture Population Structure

(Internal function) Perform the clustering process of ipcaps

clustering(
  dataframe,
  node = 1,
  result.dir,
  threshold,
  min.fst,
  method = "mix",
  min.in.group = 20,
  datatype = "snp",
  nonlinear = FALSE
)

`dataframe`	A data frame containing `raw.data` (matrix or data frame), `label` (vector), and `index` (vector). `raw.data` represents a matrix of subset of input data. `label` represents a vector of labels for all rows of `raw.data`. `index` represents a vector of indexes that are selected from the original input data.
`node`	An integer representing the current node number which is being analyzed.
`result.dir`	An output directory
`threshold`	A threshold or a cutoff to stop the ipcaps process. Also see `ipcaps` (the parameter `threshold`).
`min.fst`	A number represents a cutoff for mininum Fst value.
`method`	A clustering method selected from the `ipcaps` function. See `ipcaps` for available methods.
`min.in.group`	A integer represents a minimum number of group members.
`datatype`	To specify whether the input data are 'snp' or other type. Defalut = 'snp'.
`nonlinear`	(Unimplemented) To specify whether linear or non-linear method is used for ipcaps analysis. If TRUE, non-linear method is used, otherwise linear method is used. Default = FALSE.

A list containing status, node, and new.index as explained below:

$status is either 0 representing that the criterion is not met, or 1 representing that the criterion is met.
$node is an integer representing the current node number which is being analyzed.
$new.index is a list of vectors containing a subset of indexes split from dataframe$index according to a clustering result.