pca: Ordinary Principal Components Analysis
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

This is simply an alternative to R's principal and prcomp functions.

pca(
  x,
  ncomp = min(nrow(x) - 1, ncol(x)),
  scale = TRUE,
  method = c("dc", "std")
)

`x`	a matrix or data frame containing only numeric variables
`ncomp`	the number of components to retain.
`scale`	should the variables be scaled prior to analysis? Defaults to TRUE.
`method`	the svd algorithm to use, one of "dc" and "std" corresponding to the divide-and-conquer (the default here) and standard algorithm.
`rotate`	a rotation function from the GPArotation package. Defaults to none.