CautiousLearning-package {CautiousLearning}

Guaranteed In-Control Control Chart Performance with Cautious Parameter Learning

Description

Functions in this package allow to design, study and apply control charts based on the cautious parameter learning approach described in Capizzi and Masarotto (2019).

On system where the openMP standard is supported, these functions can take advantage of the computing power offered by a multicore workstation. See omp for the default setting.

Details

The package includes the following functions:

• Computation of the control limits via stochastic approximation: x.cl, ewma.cl, cusum.cl;

• Estimation errors and conditional run-length simulation: ruv and rcrl;

• Application to real data: cautiousLearning;

• Controlling the number of used openMP cores and the random number generator seeds: hasOMP, setOMPThreads and setSITMOSeeds.

Author(s)

Giovanna Capizzi <[e-mail]> and Guido Masarotto <[e-mail]>

Maintainer: Giovanna Capizzi

References

Capizzi, G. and Masarotto, G. (2019) "Guaranteed In-Control Control Chart Performance with Cautious Parameter Learning", accepted for publication in Journal of Quality Technology, a copy of the paper can be obtained from the authors.