Deep Neural Network Tools for Probability and Statistic Models
Activation function
Back propagation for dnn Models
Deep learning for the accelerated failure time (AFT) model
Deep learning for the generalized linear model
Deep learning for the Cox proportional hazards model
An R package for the deep neural networks probability and statistics m...
Auxiliary function for dnnFit dnnFit
Fitting a Deep Learning model with a given loss function
Specify a deep neural network model
Feed forward and back propagation for dnn Models
A function for tuning of the hyper parameters
Calculate integrated Brier Score for deepAFT
Mean Square Error (mse) for a survival Object
Functions to optimize the gradient descent of a cost function
Plot methods in dnn package
Predicted Values for a deepAFT Object
print a summary of fitted deep learning model object
Calculate Residuals for a deepAFT Fit.
The Survival Distribution
Compute a Survival Curve from a deepAFT or a deepSurv Model
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.