Fitting Deep Conditional Transformation Models

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Documentation for package ‘deeptrafo’ version 1.0-0

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atm_init Initializes the Processed Additive Predictor for ATMs
BoxCoxNN BoxCox-type neural network transformation models
coef.deeptrafo S3 methods for deep conditional transformation models
ColrNN Deep continuous outcome logistic regression
cotramNN Deep distribution-free count regression
CoxphNN Cox proportional hazards type neural network transformation models
dctm Deep conditional transformation models with alternative formula interface
deeptrafo Deep Conditional Transformation Models
ensemble.deeptrafo Deep ensembling for neural network transformation models
fitted.deeptrafo S3 methods for deep conditional transformation models
from_preds_to_trafo Define Predictor of Transformation Model
h1_init Initializes the Processed Additive Predictor for TM's Interaction
LehmanNN Lehmann-type neural network transformation models
LmNN Deep normal linear regression
logLik.deeptrafo S3 methods for deep conditional transformation models
nll Generic negative log-likelihood for transformation models
ontram Ordinal neural network transformation models
plot.deeptrafo Plot method for deep conditional transformation models
PolrNN Deep (proportional odds) logistic regression
predict.deeptrafo S3 methods for deep conditional transformation models
print.deeptrafo S3 methods for deep conditional transformation models
residuals.deeptrafo S3 methods for deep conditional transformation models
simulate.deeptrafo S3 methods for deep conditional transformation models
summary.deeptrafo S3 methods for deep conditional transformation models
SurvregNN Deep parametric survival regression
trafoensemble Transformation ensembles
trafo_control Options for transformation models
weighted_logLik Tune and evaluate weighted transformation ensembles