TreeFDR2: False Discovery Rate (FDR) Control Integrating Prior Tree...
In abichat/correlationtree: Functions about correlation tree

Modification of StructFDR::TreeFDR function to remove safety nets.

TreeFDR2(
  X,
  Y,
  tree,
  test.func,
  perm.func,
  eff.sign = TRUE,
  B = 20,
  q.cutoff = 0.5,
  alpha = 1,
  ...
)

`X`	a data matrix, rows are the features and columns are the samples.
`Y`	a vector of the phenotypic values, where association tests are being assessed.
`tree`	tree an object of `phylo` class
`test.func`	a function that performs the actual tests. It takes X, Y and ... as the inputs, and returns a list with two slots p.value and e.sign, which are vectors of p-values and signs of the effects.
`perm.func`	a function that performs the permutation tests. It takes X, Y and ... as the inputs, and returns a list with two slots X and Y, which contain the permuted data.
`eff.sign`	a logical value indicating whether the direction of the effects should be considered. If it is true (default), negative and positive effects provide conflicting information.
`B`	the number of permutations. The default is 20. If computation time is not a big concern, B=100 is suggested to achieve excellent reproducibility between different runs.
`q.cutoff`	the quantile cutoff to determine the feature sets to estimate the number of false positives under the null. This cutoff is to protect the signal part of the distributions. The default is 0.5.
`alpha`	the exponent applied to the distance matrix. Large values have more smoothing effects for closely related species. The default is 1. If the underlying structure assumption is considered to be very strong, robustness can be improved by decreasing the value to 0.5.
`...`	further arguments such as covariates to be passed to test.func