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.
|Author||Yili Hong, Yimeng Xie, and Zhibing Xu|
|Date of publication||2014-09-25 17:44:48|
|Maintainer||Yili Hong <firstname.lastname@example.org>|
ce.dat.prep: Create an object for cumulative exposure
cls: Mixed primal-dual bases algorithm for estimation of...
Coatingenv: Dynamic covariates for the coating data.
Coatingout: Dynamic covariates for coating data
deglmx: Functions for estimating parameters in the linear/nonlinear...
i.spline.x: i_spline basis
kaplan.meier.location: Kaplan-Meier Location
lifedata.MLE: Parametric Fitting for Lifetime Data
lifetime.mle: Calculate MLE for Lifetime Distribution
MIC.splines.basis: Splines basis functions
m.spline.x: M_splines basis
plev: The Standard Largest Extreme Value Distribution
plotdeglmx: Plot function for the class of "deglmx".
Prod2.fai.dat: Dataset of failure information of Product 2.
Prod2.xt.dat: Dataset of covariate information of Produce 2.
psev: The Standard Smallest Extreme Value Distribution
shock: Shock Absorber Failure Data
SPREDA-package: Statistical Package for Reliability Data Analysis
summary.Lifedata.MLE: Summaries of "Lifedata.MLE" Object