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deeptrafo (version 1.0-0)

Fitting Deep Conditional Transformation Models

Description

Allows for the specification of deep conditional transformation models (DCTMs) and ordinal neural network transformation models, as described in Baumann et al (2021) and Kook et al (2022) . Extensions such as autoregressive DCTMs (Ruegamer et al, 2023, ) and transformation ensembles (Kook et al, 2022, ) are implemented. The software package is described in Kook et al (2024, ).

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install.packages('deeptrafo')

Monthly Downloads

355

Version

1.0-0

License

GPL-3

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Maintainer

Lucas Kook

Last Published

December 3rd, 2024

Functions in deeptrafo (1.0-0)

LmNN

Deep normal linear regression
plot.deeptrafo

Plot method for deep conditional transformation models
ontram

Ordinal neural network transformation models
SurvregNN

Deep parametric survival regression
dctm

Deep conditional transformation models with alternative formula interface
cotramNN

Deep distribution-free count regression
atm_init

Initializes the Processed Additive Predictor for ATMs
PolrNN

Deep (proportional odds) logistic regression
CoxphNN

Cox proportional hazards type neural network transformation models
deeptrafo

Deep Conditional Transformation Models
ensemble.deeptrafo

Deep ensembling for neural network transformation models
from_preds_to_trafo

Define Predictor of Transformation Model
LehmanNN

Lehmann-type neural network transformation models
h1_init

Initializes the Processed Additive Predictor for TM's Interaction
coef.deeptrafo

S3 methods for deep conditional transformation models
trafo_control

Options for transformation models
trafoensemble

Transformation ensembles
nll

Generic negative log-likelihood for transformation models
weighted_logLik

Tune and evaluate weighted transformation ensembles
ColrNN

Deep continuous outcome logistic regression
BoxCoxNN

BoxCox-type neural network transformation models