Learn R Programming

kernDeepStackNet (version 2.0.2)

Kernel Deep Stacking Networks

Description

Contains functions for estimation and model selection of kernel deep stacking networks.

Copy Link

Version

Install

install.packages('kernDeepStackNet')

Monthly Downloads

71

Version

2.0.2

License

GPL-3

Maintainer

Thomas Welchowski

Last Published

May 31st, 2017

Functions in kernDeepStackNet (2.0.2)

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)