iDEMO (integrated DEgradation MOdels)

R package version 0.3-1

Introduction

The package iDEMO written in R and R GUI (graphical user interface) is a tool to build a linear degradation model which can simultaneously consider the unit-to-unit variability, within-unit variability and measurement error in the degradation paths for the Wiener degradation-based processes. The gamma and inverse Gaussian processes are considered as well. We provide the maximum likelihood estimates (MLEs) of the unknown parameters, mean-time-to-failure (MTTF) and qth quantile, and their corresponding confidence intervals based on the different information matrices. In addition, degradation model selection and goodness-of-fit tests are provided to determine and diagnose the degradation model for the user's current data by the commonly used criteria. By only enabling user interface elements when necessary, input errors are minimized.

Copyright claim

We welcome any noncommercial uses of our package for your own research, but please do not redistribute the package in any form without the authors' permission. For free resource, the authors guarantee neither the correctness of functions in this package nor responsibility for the results of analyses.

References

This page contains corresponding package that is related to the following papers:

1. Peng, C. Y. and Tseng, S. T. (2009), Mis-specification Analysis of Linear Degradation Models. IEEE Transactions on Reliability, 58(3), 444-455.

2. Cheng, Y. S. and Peng, C. Y. (2012), Integrated Degradation Models in R Using iDEMO. Journal of Statistical Software, 49(2), 1-22.

3. Peng, C. Y. (2015), Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data. Technometrics, 57(1), 100-111.

4. Peng, C. Y. and Cheng, Y. S. (2015+), Threshold Degradation in R Using iDEMO. in Computational Network Analysis with R, to be published by John Wiley & Sons, New York.

We will improve this package from time to time. Newer version will be released here. If you have any comments and questions, please send to chienyu@stat.sinica.edu.tw.

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Last modified: 2015-06-22