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.
| Version: |
3.0.0 |
| Depends: |
R (≥ 2.10) |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2026-07-07 |
| DOI: |
10.32614/CRAN.package.MMAD |
| Author: |
Xifen Huang [aut],
Jinfeng Xu [aut],
Jiaqi Gu [aut, cre] |
| Maintainer: |
Jiaqi Gu <jiaqigu at usf.edu> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
MMAD results |
Documentation:
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