Learn R Programming

⚠️There's a newer version (1.1.46) of this package.Take me there.

blackbox (version 1.0)

Black Box Optimization and Exploration of Parameter Space

Description

Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. blackbox 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.

Copy Link

Version

Install

install.packages('blackbox')

Monthly Downloads

2,199

Version

1.0

License

CeCILL-2

Maintainer

Franois Rousset

Last Published

April 14th, 2016

Functions in blackbox (1.0)

bboptim

Black-box function optimization
calc1Dprofiles

One and two-dimensional profiles, and surface plots
buildFONKgpointls

Prepare data for smoothing
blackbox

Black box optimization and response surface exploration
init_grid

Define starting points in parameter space.
preprocessbboptions

Set controls for most functiosn in the package
prepareData

Prepare data and controls for smoothing
sampleByResp

Sample predictor points according to predicted response
saveOldFile

Save a copy of an existing file.
calc1DCIs

Compute 1D confidence intervals
maximizeOK

Find maximum of predicted response surface
ordinary-internal

Internal ordinary Functions
options

blackbox options settings
buildPointls

Read a data file
calcPredictorOK

Generate smoothing predictor given smoothing parameters
islogscale

Test for parameter log scale
writeFinalInfo

Pretty output, and management of output files
calcLRTs

Compute (profile) likelihood ratio tests
calcGCV

Estimate smoothing parameters by generalized cross-validation (GCV)