Package: dnn 0.0.7

dnn: Deep Neural Network Tools for Probability and Statistic Models

Contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.

Authors:Bingshu E. Chen [aut, cre], Patrick Norman [aut, ctb], Wenyu Jiang [ctb], Wanlu Li [ctb]

dnn_0.0.7.tar.gz
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dnn_0.0.7.tgz(r-4.6-x86_64)dnn_0.0.7.tgz(r-4.6-arm64)dnn_0.0.7.tgz(r-4.5-x86_64)dnn_0.0.7.tgz(r-4.5-arm64)
dnn_0.0.7.tar.gz(r-4.7-arm64)dnn_0.0.7.tar.gz(r-4.6-arm64)dnn_0.0.7.tar.gz(r-4.7-x86_64)dnn_0.0.7.tar.gz(r-4.6-x86_64)
dnn_0.0.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
dnn/json (API)

# Install 'dnn' in R:
install.packages('dnn', repos = c('https://statapps.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cppopenmp

1.08 score 12 scripts 511 downloads 55 exports 24 dependencies

Last updated from:733b53ddd9. Checks:11 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK146
source / vignettesOK168
linux-release-x86_64OK183
macos-release-arm64OK126
macos-release-x86_64OK277
macos-oldrel-arm64OK139
macos-oldrel-x86_64OK298
windows-develOK130
windows-releaseOK133
windows-oldrelOK123
wasm-releaseOK107

Exports:bwdCheckbwdNNbwdNN2CVpredErrdeepAFTdeepAFT.defaultdeepAFT.formuladeepAFT.ipcwdeepAFT.transdeepGlmdeepSurvdeepSurv.defaultdeludidudlreludnnControldnnFitdnnFit2dNNmodeldreludsigmoiddtanhelufwdNNfwdNN2hyperTuningibs.deepAFTidulrelumseIPCWoptimizerAdamGoptimizerMomentumoptimizerNAGplot.deepAFTplot.dNNmodelpredict.dNNmodelpredict.dSurvprint.deepAFTprint.deepGlmprint.deepSurvprint.dNNmodelprint.summary.deepAFTprint.summary.deepGlmprint.summary.deepSurvreluresiduals.deepGlmresiduals.dSurvrmst.deepSurvsigmoidsummary.deepAFTsummary.deepGlmsummary.deepSurvsummary.dNNmodelsurvfit.deepAFTsurvfit.deepSurv

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglatticelifecyclelplMASSMatrixR6RColorBrewerRcppRcppArmadillorlangS7scalessurvivalvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
An R package for the deep neural networks probability and statistics modelsdnn-package dnn dnn-doc
Activation functionactivation delu didu dlrelu drelu dsigmoid dtanh elu idu lrelu relu sigmoid
Back propagation for dnn ModelsbwdCheck bwdNN bwdNN2
Deep learning for the accelerated failure time (AFT) modeldeepAFT deepAFT.default deepAFT.formula deepAFT.ipcw deepAFT.trans
Deep learning for the generalized linear modelsdeepGLM deepGlm predict.deepGlm residuals.deepGlm summary.deepGlm
Deep learning for the Cox proportional hazards modeldeepSurv deepSurv.default summary.deepSurv
Auxiliary function for 'dnnFit' dnnFitdnnControl
Fitting a Deep Learning model with a given loss functiondnnFit dnnFit2
Specify a deep neural network modeldNNmodel
Feed forward and back propagation for dnn ModelsfwdNN fwdNN2 predict.dNNmodel
A function for tuning of the hyper parametersCVpredErr hyperTuning
Calculate integrated Brier Score for deepAFTibs.deepAFT
Mean Square Error (mse) for a fitted survival ObjectmseIPCW
Functions to optimize the gradient descent of a cost functionoptimizerAdamG optimizerMomentum optimizerNAG optimizerSGD
Plot methods in dnn packageplot.deepAFT plot.dNNmodel
Predicted Values for a deepAFT or a deepSurv Objectpredict.deepAFT predict.deepSurv predict.dSurv
print a summary of fitted deep learning model objectprint.deepAFT print.deepGlm print.deepSurv print.dNNmodel print.summary.deepAFT print.summary.deepGlm print.summary.deepSurv print.summary.dNNmodel summary.deepAFT summary.dNNmodel
Calculate Residuals for a deepAFT Fit.residuals.deepAFT residuals.dSurv
The restricted mean survival time (RMST)rmst.deepSurv
Compute a Survival Curve from a deepAFT or a deepSurv Modelsurvfit.deepAFT survfit.deepSurv