Package: MMAD 1.0.0

Dengge Liu

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>.

Authors:Xifen Huang [aut], Dengge Liu [aut, cre], Yunpeng Zhou [ctb]

MMAD_1.0.0.tar.gz
MMAD_1.0.0.tar.gz(r-4.5-noble)MMAD_1.0.0.tar.gz(r-4.4-noble)
MMAD_1.0.0.tgz(r-4.4-emscripten)MMAD_1.0.0.tgz(r-4.3-emscripten)
MMAD.pdf |MMAD.html✨
MMAD/json (API)

# Install 'MMAD' in R:
install.packages('MMAD', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • bcos - Breast Cosmesis Data
  • cadi - The children’s absenteeism data in Indonesia
  • kidney - Kidney Infection Data
  • lung - NCCTG Lung Cancer Data
  • vijc - Voluntary and involuntary job changes data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 3 scripts 118 downloads 8 exports 3 dependencies

Last updated 1 years agofrom:f64763229c. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-linuxOKNov 06 2024

Exports:CoxMMCZIGPMMGaFrailtyMMIC2ControlIC2MMIC2ProLTNMMZIGPMM

Dependencies:latticeMatrixsurvival