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dnn (version 0.0.6)

Deep Neural Network Tools for Probability and Statistic Models

Description

Contains tools to build deep neural network with flexible users define loss function and probability models. Several applications included in this package are, 1) The (deepAFT) model, a deep neural network model for accelerated failure time (AFT) model for survival data. 2) The (deepGLM) model, a deep neural network model for generalized linear model (glm) for continuous, categorical and Poisson data.

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Version

Install

install.packages('dnn')

Monthly Downloads

425

Version

0.0.6

License

GPL (>= 2)

Maintainer

Bingshu Chen

Last Published

March 14th, 2024

Functions in dnn (0.0.6)

print

print a summary of fitted deep learning model object
msePICW

Mean Square Error (mse) for a survival Object
rsurv

The Survival Distribution
residuals

Calculate Residuals for a deepAFT Fit.
hyperTuning

A function for tuning of the hyper parameters
ibs

Calculate integrated Brier Score for deepAFT
survfit

Compute a Survival Curve from a deepAFT or a deepSurv Model
optimizerSGD

Functions to optimize the gradient descent of a cost function
plot

Plot methods in dnn package
predict

Predicted Values for a deepAFT Object
dnnControl

Auxiliary function for dnnFit dnnFit
deepAFT

Deep learning for the accelerated failure time (AFT) model
fwdNN

Feed forward and back propagation for dnn Models
deepSurv

Deep learning for the Cox proportional hazards model
dnn-package

An R package for the deep neural networks probability and statistics models
dnnFit

Fitting a Deep Learning model with a given loss function
dNNmodel

Specify a deep neural network model
activation

Activation function
deepGLM

Deep learning for the generalized linear model
bwdNN

Back propagation for dnn Models