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.
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.
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.