In practical applications of control charts the in-control state and the corresponding chart
parameters are usually estimated based on some past in-control data. The estimation error then
needs to be accounted for. In this paper we present an R package,
spcadjust
, which implements a
bootstrap based method for adjusting monitoring schemes to take into account the estimation error.
By bootstrapping the past data this method guarantees, with a certain probability, a conditional
performance of the chart. In
spcadjust
the method is implement for various types of Shewhart,
CUSUM and EWMA charts, various performance criteria, and both parametric and non-parametric
bootstrap schemes. In addition to the basic charts, charts based on linear and logistic regression
models for risk adjusted monitoring are included, and it is easy for the user to add further charts. Use
of the package is demonstrated by examples.