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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 |
| Reference manual: | MMAD.html , MMAD.pdf |
| Package source: | MMAD_3.0.0.tar.gz |
| Windows binaries: | r-devel: MMAD_3.0.0.zip, r-release: MMAD_3.0.0.zip, r-oldrel: MMAD_3.0.0.zip |
| macOS binaries: | r-release (arm64): MMAD_3.0.0.tgz, r-oldrel (arm64): MMAD_3.0.0.tgz, r-release (x86_64): MMAD_3.0.0.tgz, r-oldrel (x86_64): MMAD_3.0.0.tgz |
| Old sources: | MMAD archive |
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