Package: MMAD 3.0.0

MMAD: Minorization-Maximization via Assembly-Decomposition Technology

A formula-driven framework for maximizing target functions via the minorization-maximization (MM) algorithm. The package represents the target as a symbolic expression tree, infers its curvature via disciplined-convex-programming rules, and constructs a separable surrogate at each iterate using only Jensen's inequality and the supporting hyperplane. The driver maximizes the surrogate via block-coordinate Newton with line search, falling back to a multivariate step on any non-separable residue. A formula interface accepts standard R expressions (including `sum()` reductions and `X %*% theta` design-matrix products) so statistical models such as Poisson regression can be written in one line.

Authors:Xifen Huang [aut], Jinfeng Xu [aut], Jiaqi Gu [aut, cre]