phd: PHD SDR fitting function
In hierSDR: Hierarchical Sufficient Dimension Reduction

fits SDR models (PHD approach)

1	phd(x, y, d = 5L)

`x`	an n x p matrix of covariates, where each row is an observation and each column is a predictor
`y`	vector of responses of length n
`d`	an integer representing the structural dimension

A list with the following elements

beta.hat estimated sufficient dimension reduction matrix
eta.hat coefficients on the scale of the scaled covariates
cov variance covariance matric for the covariates
sqrt.inv.cov inverse square root of the variance covariance matrix for the covariates. Used for scaling
M matrix from principal Hessian directions
eigenvalues eigenvalues of the M matrix