Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.
|Author||François Rousset [aut, cre, cph], Leblois Raphaël [ctb]|
|Date of publication||2016-07-10 15:39:41|
|Maintainer||François Rousset <firstname.lastname@example.org>|
bboptim: Black-box function optimization
blackbox: Black box optimization and response surface exploration
blackbox-internal: Internal ordinary Functions
buildFONKgpointls: Prepare data for smoothing
buildPointls: Read a data file
calc1DCIs: Compute 1D confidence intervals
calc1Dprofiles: One and two-dimensional profiles, and surface plots
calcGCV: Estimate smoothing parameters by generalized cross-validation...
calcLRTs: Compute (profile) likelihood ratio tests
calcPredictorOK: Generate smoothing predictor given smoothing parameters
init_grid: Define starting points in parameter space.
islogscale: Test for parameter log scale
maximizeOK: Find maximum of predicted response surface
options: blackbox options settings
prepareData: Prepare data and controls for smoothing
preprocessbboptions: Set controls for most functiosn in the package
sampleByResp: Sample predictor points according to predicted response
saveOldFile: Save a copy of an existing file.
writeFinalInfo: Pretty output, and management of output files