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kernDeepStackNet (version 2.0.2)
Kernel Deep Stacking Networks
Description
Contains functions for estimation and model selection of kernel deep stacking networks.
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2.0.2
2.0.1
2.0.0
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Install
install.packages('kernDeepStackNet')
Monthly Downloads
55
Version
2.0.2
License
GPL-3
Maintainer
Thomas Welchowski
Last Published
May 31st, 2017
Functions in kernDeepStackNet (2.0.2)
Search functions
EImod
Expected improvement criterion replacement function
calcTrA
Calculates the trace of the hat matrix
cancorRed
Calculate first canonical correlation
crossprodRcpp
Calculates the cross product of a matrix
devStandard
Predictive deviance of a linear model
fineTuneCvKDSN
Fine tuning of random weights of a given KDSN model
fitEnsembleKDSN
Fit an ensemble of KDSN (experimental)
fitKDSN
Fit kernel deep stacking network with random Fourier transformations
calcTrAFast
Calculates the trace of the hat matrix as C version
calcWdiag
Calculation of weight matrix
fourierTransPredict
Prediction based on random Fourier transformation
gDerivMu
Derivative of the link function evaluated at the expected values
randomFourierTrans
Random Fourier transformation
rdcPart
Randomized dependence coefficient partial calculation
varMu
Variance function evaluated at expected value
tuneMboLevelCvKDSN
Tuning of KDSN with efficient global optimization given level by cross-validation
tuneMboLevelGcvKDSN
Tuning of KDSN with efficient global optimization given level by cross-validation
lossApprox
Kernel deep stacking network loss function
lossCvKDSN
Kernel deep stacking network loss function based on cross-validation
lossSharedTestKDSN
Kernel deep stacking network loss function with test set and shared hyperparameters
mbo1d
Efficient global optimization with iterative point proposals
tuneMboSharedCvKDSN
Tuning of KDSN with efficient global optimization given level by cross-validation and shared hyperparameters
tuneMboSharedSubsetKDSN
Tuning subsets of KDSN with efficient global optimization and shared hyperparameters (experimental)
getEigenValuesRcpp
Calculates the eigenvalues of a matrix
kernDeepStackNet-package
Kernel deep stacking networks with random Fourier transformation
predict.KDSNensemble
Predict kernel deep stacking networks ensembles (experimental)
predict.KDSNensembleDisk
Predict kernel deep stacking networks ensembles (experimental)
lossGCV
Generalized cross-validation loss
lossSharedCvKDSN
Kernel deep stacking network loss function based on cross-validation and shared hyperparameters
mboAll
Efficient global optimization inclusive meta model validation
optimize1dMulti
One dimensional optimization of multivariate loss functions
rdcVarSelSubset
Variable selection based on RDC with genetic algorithm (experimental)
robustStandard
Robust standardization
kernDeepStackNet_crossprodRcpp
Calculates the cross product of a matrix
kernDeepStackNet_getEigenValuesRcpp
Calculates the eigenvalues of a matrix
predLogProb
Predictive logarithmic probability of Kriging model
predict.KDSN
Predict kernel deep stacking networks
rdcSubset
Randomized dependence coefficients score on given subset
rdcVarOrder
Variable ordering using randomized dependence coefficients (experimental)