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 |
| DOI: |
10.32614/CRAN.package.MMAD |
| 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 |
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