MMAD: An R Package of Minorization-Maximization Algorithm via the
Assembly–Decomposition Technology
The minorization-maximization (MM) algorithm is a powerful tool for maximizing nonconcave target function. However, for most existing MM algorithms, the surrogate function in the minorization step is constructed in a case-specific manner and requires manual programming. To address this limitation, we develop the R package MMAD, which systematically integrates the assembly–decomposition technology in the MM framework. This new package provides a comprehensive computational toolkit for one-stop inference of complex target functions, including function construction, evaluation, minorization and optimization via MM algorithm. By representing the target function through a hierarchical composition of assembly functions, we design a hierarchical algorithmic structure that supports both bottom-up operations (construction, evaluation) and top-down operation (minorization).
| Version: |
2.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
utils |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2025-11-26 |
| DOI: |
10.32614/CRAN.package.MMAD |
| Author: |
Jiaqi Gu [aut, cre] |
| Maintainer: |
Jiaqi Gu <jiaqigu at usf.edu> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
MMAD results |
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