# Upper Clopper-Pearson Confidence Limits For Burn-in Studies under Additional Available Information Functions to compute upper Clopper-Pearson confidence limits of early life failure probabilities and required sample sizes of burn-in studies under further available information, e.g. from other products or technologies. package ## Details ||| |:--|:--| |Package:|AdvBinomApps| |Type:|Package| |Version:|1.0| |Date:|2016-04-05| |License:|GPL-3| ## Author(s) Daniel Kurz, Horst Lewitschnig Maintainer: Horst Lewitschnig horst.lewitschnig@infineon.com ## References C.J. Clopper and E.S. Pearson: **The use of confidence or fiducial limits illustrated in the case of the binomial**. Biometrika, 26(4): 404-413, 1934. D. Kurz, H. Lewitschnig and J. Pilz: **Decision-Theoretical Model for Failures Tackled by Countermeasures**. IEEE Transactions on Reliability, 63(2): 583-592, 2014. DOI: 10.1109/TR.2014.2315952. D. Kurz, H. Lewitschnig and J. Pilz: **Failure probability estimation under additional subsystem information with application to semiconductor burn-in**. Resubmitted to: Journal of Applied Statistics, 2015. D. Kurz, H. Lewitschnig and J. Pilz: **An Advanced Area Scaling Approach for Semiconductor Burn-in**. Microelectronics Reliability, 55(1): 129-137, 2015. DOI: 10.1016/j.microrel.2014.09.007. D. Kurz, H. Lewitschnig and J. Pilz: **Failure Probability Estimation with Differently Sized Reference Products for Semiconductor Burn-in Studies**. Applied Stochastic Models in Business and Industry, 31(5): 732-744, 2015. DOI: 10.1002/asmb.2100. **Acknowledgment** The work has been performed in the project EPT300, co-funded by grants from Austria, Germany, Italy, The Netherlands and the ENIAC Joint Undertaking. This project is co-funded within the programme "Forschung, Innovation und Technologie fuer Informationstechnologie" by the Austrian Ministry for Transport, Innovation and Technology. ## See Also `GenBinomApps` ## Examples ```r #MULTIPLE REFERENCE PRODUCTS: k<-c(1,2) n<-c(110000,138000) A.ref<-c(5.21,10.71) A.follow<-8.5 p.target<-20e-06 ci.mult.ref(k,n,A.ref,A.follow,p.target=p.target) #SYNERGIES k<-c(0,1) n<-c(110000,330000) ci.syn(k,n,0.1,20e-06) #SEPARATE AREA SCALING k<-c(1,0) n<-110000 A.ref<-c(1,1.5) A.follow<-c(1,2) p.target<-20e-06 ci.sas(k,n,A.ref,A.follow,0.1,p.target) ```

AdvBinomApps-package