Find the best fit correction for the light curve.
One or more design matrices. Each matrix must have a shape of
(time, regressors). The columns contained in each matrix must be
known to correlate with additive noise components we want to remove
from the light curve.
Mask, where True indicates a cadence that should be used.
Standard deviation at which to remove outliers from fitting
Number of iterations to fit and remove outliers
Whether to propagate the uncertainties from the regression. Default is False.
Setting to True will increase run time, but will sample from multivariate normal
distribution of weights.
Corrected light curve, with noise removed.