R: Fitting Deep Conditional Transformation Models

# Fitting Deep Conditional Transformation Models

## Documentation for package ‘deeptrafo’ version 0.1-1

- DESCRIPTION file.

## Help Pages

atm_init | Initializes the Processed Additive Predictor for ATMs
BoxCoxNN | BoxCox-type neural network transformation models
coef.deeptrafo | Generic methods for neural network 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 | Generic methods for neural network 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 | Generic methods for neural network transformation models
nll | Generic negative log-likelihood for transformation models
ontram | Ordinal neural network transformation models
plot.deeptrafo | Generic methods for neural network transformation models
PolrNN | Deep (proportional odds) logistic regression
predict.deeptrafo | Generic methods for neural network transformation models
print.deeptrafo | Generic methods for neural network transformation models
simulate.deeptrafo | Generic methods for neural network transformation models
summary.deeptrafo | Generic methods for neural network transformation models
SurvregNN | Deep parametric survival regression
trafo_control | Options for transformation models