The goal of 'HAMMER' is to provide factor analytic representation
learning and associated determinacy metrics for very-high-dimensional data.
It projects high-dimensional data onto low-dimensional generative latent
sources and assesses the uncertainty in the projection. The projection is
distribution-free, scale-equivariant, and efficient. For details, see
Peeters (2026)