pfa: Principal Factors Analysis (Principal Axis Factoring)
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

This implements the original factor analysis method, principal axis factoring. The only augmentation besides rotation of the loadings is shrinkage can be used to acheive a more numerically stable fit, particuarly under high dimensional settings. When n < p it is switched on automatically.

pfa(
  Y,
  nfac = min(nrow(Y) - 3, ncol(Y) - 2),
  rotate = Varimax,
  scale = T,
  corr = c("pearson", "robust", "spearman"),
  max.iter = 4000,
  tol = 1e-12
)

`Y`	a numeric matrix or data frame of only numeric variables.
`nfac`	the number of factors to attempt to extract.
`rotate`	a rotation function from the GPArotation package. Defaults to Varimax.
`scale`	should the variables be scaled prior to analysis? Defaults to TRUE.
`corr`	one of "pearson", "robust", or "spearman".
`max.iter`	maximum number of iterations. defaults to 4000.
`tol`	a tolerance value for convergence. defaults to 1e-12.
`shrink`	if TRUE, the adaptive non-linear shrinkage method from the `covShrink` function is used. It is used regardless of the setting here if n < p.