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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 |
| Reference manual: | MMAD.html , MMAD.pdf |
| Package source: | MMAD_2.0.tar.gz |
| Windows binaries: | r-devel: MMAD_2.0.zip, r-release: MMAD_2.0.zip, r-oldrel: MMAD_2.0.zip |
| macOS binaries: | r-release (arm64): MMAD_2.0.tgz, r-oldrel (arm64): MMAD_2.0.tgz, r-release (x86_64): MMAD_2.0.tgz, r-oldrel (x86_64): MMAD_2.0.tgz |
| Old sources: | MMAD archive |
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