Package: dnn 0.0.6

Bingshu E. Chen

dnn: Deep Neural Network Tools for Probability and Statistic Models

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.

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

dnn_0.0.6.tar.gz
dnn_0.0.6.tar.gz(r-4.5-noble)dnn_0.0.6.tar.gz(r-4.4-noble)
dnn_0.0.6.tgz(r-4.4-emscripten)dnn_0.0.6.tgz(r-4.3-emscripten)
dnn.pdf |dnn.html✨
dnn/json (API)

# Install 'dnn' in R:
install.packages('dnn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

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

1.00 score 4 scripts 167 downloads 60 exports 31 dependencies

Last updated 9 months agofrom:7919f678eb. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-linux-x86_64NOTENov 10 2024

Exports:bwdCheckbwdNNbwdNN2CVpredErrdeepAFTdeepAFT.defaultdeepAFT.formuladeepAFT.ipcwdeepAFT.transdeepGlmdeepSurvdeepSurv.defaultdeludidudlreludnnControldnnFitdnnFit2dNNmodeldreludsigmoiddsurvdtanhelufwdNNfwdNN2hyperTuningibsibs.deepAFTibs.defaultidulrelumseIPCWoptimizerAdamGoptimizerMomentumoptimizerNAGplot.deepAFTplot.dNNmodelpredict.dNNmodelpredict.dSurvprint.deepAFTprint.deepGlmprint.deepSurvprint.dNNmodelprint.summary.deepAFTprint.summary.deepGlmprint.summary.deepSurvpsurvqsurvrcoxphreluresiduals.deepGlmresiduals.dSurvrsurvsigmoidsummary.deepAFTsummary.deepGlmsummary.deepSurvsummary.dNNmodelsurvfit.dSurv

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalessurvivaltibbleutf8vctrsviridisLitewithr

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 modeldeepGLM 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 ibs.deepAFT ibs.default
Mean Square Error (mse) for a 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 Objectpredict.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 Survival Distributiondsurv psurv qsurv rcoxph rSurv rsurv
Compute a Survival Curve from a deepAFT or a deepSurv Modelsurvfit.dSurv