pcensmix-package: Generate and fit a model to censored mixture data

In pcensmix: Model Fitting to Progressively Censored Mixture Data

Description

This package provides functions to generate two-component mixture data from various different mixture distribution, generate progressive Type-II censored data in a mixture structure and fit a normal mixture model using a constrained EM algorithm. In addition, it can create a progressive Type-II censored version of a given real dataset and fit a normal mixture model to. Main functions are pcgen, pcensmixSim and pcensmixR. Example datasets are included for illustration.

Details

Package: pcensmix

Type: Package

Version: 1.2-1

Date: 2017-07-24

License: GPL (>= 2)

Author(s)

Lida Fallah <[e-mail]> and John Hinde

Maintainer: Lida Fallah <[e-mail]>

References

Aitkin, M., Francis, B., Hinde, J. and Darnell, R., (2009). Statistical Modelling in R. Oxford: Oxford University Press.

Balakrishnan, N. and Aggarwala, R., (2000). Progressive Censoring: Theory, Methods, and Applications. Springer Science & Business Media.

Hathaway, R.J., (1985). A constrained formulation of maximum-likelihood estimation for normal mixture distributions. The Annals of Statistics, 795-800.

McLachlan, G. and Krishnan, T., (2007). The EM Algorithm and Extensions. John Wiley & Sons.

McLachlan, G. and Peel, D., (2004). Finite Mixture Models. John Wiley & Sons.

pcensmix documentation built on May 2, 2019, 1:10 p.m.