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>.

Getting started

Package details

AuthorXifen Huang [aut], Dengge Liu [aut, cre], Yunpeng Zhou [ctb]
MaintainerDengge Liu <dongge_adam@126.com>
LicenseGPL (>= 3)
Version1.0.0
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("MMAD")

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MMAD documentation built on July 9, 2023, 5:13 p.m.