Version 0.1.3

0.1.3 (2024-03-07)

• RPCA algorithms now start with a normalizing scaler
• The EM algorithms now include a gradient projection step to be more robust to colinearity
• The EM algorithm based on the Gaussian model is now initialized using a robust estimation of the covariance matrix
• A bug in the EM algorithm has been patched: the normalizing matrix gamma was creating a sampling biais
• Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method
• The ImputeRegressor class now handles the nans by row by default
• The metric frechet was not correctly called and has been patched
• The EM algorithm with VAR(p) now fills initial holes in order to avoid exponential explosions