Description Details Author(s) References

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.

Package: | SPREDA |

Type: | Package |

Version: | 1.0 |

Date: | 2014-09-20 |

License: | GPL-2 |

Contains functions that are useful for the analysis of reliability data.

Yili Hong, Yimeng Xie, and Zhibing Xu

Maintainer: Yili Hong <[email protected]>

Hong, Y., W. Q. Meeker, and J. D. McCalley (2009). Prediction of Remaining Life of Power Transformers Based on Left Truncated and Right Censored Lifetime Data. *The Annals of Applied Statistics*, Vol. 3, pp. 857-879.

Hong, Y. and Meeker, W. Q. (2010), Field-Failure and Warranty Prediction Using Auxiliary Use-rate Data. *Technometrics*, Vol. 52, pp. 148-159.

Hong, Y. and Meeker, W. Q. (2013), Field-Failure Predictions Based on Failure-time Data with Dynamic Covariate Information, *Technometrics*, Vol. 55, pp. 135-149.

Hong, Y. (2013), On Computing the Distribution Function for the Poisson Binomial Distribution, *Computational Statistics and Data Analysis*, Vol. 59, pp. 41-51.

Hong. Y., Y. Duan, W. Q. Meeker, D. L. Stanley, and X. Gu (2014), Statistical Methods for Degradation Data with Dynamic Covariates Information and an Application to Outdoor Weathering Data, *Technometrics*, DOI: 10.1080/00401706.2014.915891.

Meeker, W. Q. and L. A. Escobar (1998). Statistical Methods for Reliability Data. *John Wiley & Sons*.

Meeker, W. Q. and L. A. Escobar (2014). RSplida. http://www.public.iastate.edu/~stat533/.

Xu, Z., Y. Hong, and R. Jin (2014), Nonlinear General Path Models for Degradation Data with Dynamic Covariates, submitted.

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