MMAD: MM Algorithm Based on the Assembly-Decomposition Technology

The Minorize-Maximization(MM) algorithm based on Assembly-Decomposition(AD) technology can be used for model estimation of parametric models, semi-parametric models and non-parametric models. We selected parametric models including left truncated normal distribution, type I multivariate zero-inflated generalized poisson distribution and multivariate compound zero-inflated generalized poisson distribution; semiparametric models include Cox model and gamma frailty model; nonparametric model is estimated for type II interval-censored data. These general methods are proposed based on the following papers, Tian, Huang and Xu (2019) <doi:10.5705/SS.202016.0488>, Huang, Xu and Tian (2019) <doi:10.5705/ss.202016.0516>, Zhang and Huang (2022) <doi:10.1117/12.2642737>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: stats, grDevices, survival
Published: 2023-07-08
Author: Xifen Huang [aut], Dengge Liu [aut, cre], Yunpeng Zhou [ctb]
Maintainer: Dengge Liu <dongge_adam at 126.com>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: MMAD results

Documentation:

Reference manual: MMAD.pdf

Downloads:

Package source: MMAD_1.0.0.tar.gz
Windows binaries: r-devel: MMAD_1.0.0.zip, r-release: MMAD_1.0.0.zip, r-oldrel: MMAD_1.0.0.zip
macOS binaries: r-release (arm64): MMAD_1.0.0.tgz, r-oldrel (arm64): MMAD_1.0.0.tgz, r-release (x86_64): MMAD_1.0.0.tgz

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