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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:f64763229c. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 06 2024 |
R-4.5-linux | OK | Dec 06 2024 |
Exports:CoxMMCZIGPMMGaFrailtyMMIC2ControlIC2MMIC2ProLTNMMZIGPMM