Deep Neural Network Tools for Probability and Statistic Models

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Documentation for package ‘dnn’ version 0.0.6

• DESCRIPTION file.

Help Pages

dnn-package	An R package for the deep neural networks probability and statistics models
activation	Activation function
bwdCheck	Back propagation for dnn Models
bwdNN	Back propagation for dnn Models
bwdNN2	Back propagation for dnn Models
CVpredErr	A function for tuning of the hyper parameters
deepAFT	Deep learning for the accelerated failure time (AFT) model
deepAFT.default	Deep learning for the accelerated failure time (AFT) model
deepAFT.formula	Deep learning for the accelerated failure time (AFT) model
deepAFT.ipcw	Deep learning for the accelerated failure time (AFT) model
deepAFT.trans	Deep learning for the accelerated failure time (AFT) model
deepGLM	Deep learning for the generalized linear model
deepGlm	Deep learning for the generalized linear model
deepSurv	Deep learning for the Cox proportional hazards model
deepSurv.default	Deep learning for the Cox proportional hazards model
delu	Activation function
didu	Activation function
dlrelu	Activation function
dnn	An R package for the deep neural networks probability and statistics models
dnn-doc	An R package for the deep neural networks probability and statistics models
dnnControl	Auxiliary function for 'dnnFit' dnnFit
dnnFit	Fitting a Deep Learning model with a given loss function
dnnFit2	Fitting a Deep Learning model with a given loss function
dNNmodel	Specify a deep neural network model
drelu	Activation function
dsigmoid	Activation function
dsurv	The Survival Distribution
dtanh	Activation function
elu	Activation function
fwdNN	Feed forward and back propagation for dnn Models
fwdNN2	Feed forward and back propagation for dnn Models
hyperTuning	A function for tuning of the hyper parameters
ibs	Calculate integrated Brier Score for deepAFT
ibs.deepAFT	Calculate integrated Brier Score for deepAFT
ibs.default	Calculate integrated Brier Score for deepAFT
idu	Activation function
lrelu	Activation function
mseIPCW	Mean Square Error (mse) for a survival Object
optimizerAdamG	Functions to optimize the gradient descent of a cost function
optimizerMomentum	Functions to optimize the gradient descent of a cost function
optimizerNAG	Functions to optimize the gradient descent of a cost function
optimizerSGD	Functions to optimize the gradient descent of a cost function
plot.deepAFT	Plot methods in dnn package
plot.dNNmodel	Plot methods in dnn package
predict.deepGlm	Deep learning for the generalized linear model
predict.dNNmodel	Feed forward and back propagation for dnn Models
predict.dSurv	Predicted Values for a deepAFT Object
print.deepAFT	print a summary of fitted deep learning model object
print.deepGlm	print a summary of fitted deep learning model object
print.deepSurv	print a summary of fitted deep learning model object
print.dNNmodel	print a summary of fitted deep learning model object
print.summary.deepAFT	print a summary of fitted deep learning model object
print.summary.deepGlm	print a summary of fitted deep learning model object
print.summary.deepSurv	print a summary of fitted deep learning model object
print.summary.dNNmodel	print a summary of fitted deep learning model object
psurv	The Survival Distribution
qsurv	The Survival Distribution
rcoxph	The Survival Distribution
relu	Activation function
residuals.deepAFT	Calculate Residuals for a deepAFT Fit.
residuals.deepGlm	Deep learning for the generalized linear model
residuals.dSurv	Calculate Residuals for a deepAFT Fit.
rSurv	The Survival Distribution
rsurv	The Survival Distribution
sigmoid	Activation function
summary.deepAFT	print a summary of fitted deep learning model object
summary.deepGlm	Deep learning for the generalized linear model
summary.deepSurv	Deep learning for the Cox proportional hazards model
summary.dNNmodel	print a summary of fitted deep learning model object
survfit.dSurv	Compute a Survival Curve from a deepAFT or a deepSurv Model