Predict the results of a future scheduled inspection and update the state accordingly. The state prior to inspection is utilized to determine the likelihood of finding each particle, and the state after inspection consists of a combination of missed particles and repaired particles. The Probability of Crack Detection (PCD) results of this inspection are appended to the previously existing PCD results (if any).
Creates an object of class Sing, Mult, or CD from an appropriate list of parameters (using parameter analysis.type to determine the output class). The list of parameters may or may not be of class crackRparameters (the function will attempt to do its thing regardless). User calling of this function is optional since an object of crackRparameters (or simply an appropriate list) may be directly passed to analyze, which will run crackRinit if this was not previously done.
Advance in time through a single inspection interval, adjusting the particle weights and reporting failure probabilities along the way. Estimates of Single Flight Probability Of Failure (SFPOF) will be calculated at intervals specified in the parameter flt.calc.interval, along with the bootstrap estimates of quantiles of SFPOF (if bootstrap.sfpof is TRUE) and an estimate of the probability of failure for each subinterval.
One powerful method for assessing convergence of a sequential importance sampling analysis is to run parallel sequences. This function does this, saving only the results (of class crackRresults). Results are returned in a list. Note parallelization is not performed, the analyses are run sequentially...thus this is a convenience function and a placeholder for the time being.
The POD curve in probabilistic damage tolerance analysis is often defined using the formulation of a Log-Normal CDF, with several optional modifying parameters. This function can be used to generate the appropriate POD function for use in the crackRparameters component "pod.func".
This is the main analysis function for the sequential importance sampling approach to probabilistic damage tolerance analysis in the crackR package. User can enter an object of class crackRparameters or class crackR. If a crackRparameters object is entered, crackRinit will be run to initialize a crackR object. The sample size for an existing crackR object can be increased by utilizing an existing crackR object and specifying add=TRUE.
Using a sampling-based approach (either sequential importance sampling explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
The main focus of the crackR package is on the sequential importance sampling approach to probabilistic damage tolerance analysis. As part of the work creating that approach, an explicit sampling routine was created for validation of results. It proceeds by repeatedly simulating the life cycle, flight-by-flight, and finding the first flight to failure for each trial. This approach requires many millions of samples to yield useful SFPOF estimates, but provided a sanity check for the results of the sequential importance sampling routine. Scheduled inspections may be included. If there are no scheduled inspections, the user may utilize importance sampling to set the initial state and drastically speed up convergence of the SFPOF estimates. The parameters for running this analysis are the same as those of the sequential importance sampling routine.
crackRmc will calculate SFPOF on the intervals specified for that function, along with the raw results of the simulation. If a different set of flight intervals for SFPOF is desired, this function can be used to do so without re-running the simulation.
providing 
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