Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo

Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo

: deep learning, distributional regression, neural networks, transformation models

Contemporary empirical applications frequently require flexible regression models for
complex response types and large tabular or non-tabular, including image or text, data.
Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems
tractable. Here, we present deeptrafo, a package for fitting flexible regression models for
conditional distributions using a tensorflow back end with numerous additional processors,
such as neural networks, penalties, and smoothing splines. Package deeptrafo implements
deep conditional transformation models (DCTMs) for binary, ordinal, count, survival,
continuous, and time series responses, potentially with uninformative censoring. Unlike
other available methods, DCTMs do not assume a parametric family of distributions for
the response. Further, the data analyst may trade off interpretability and flexibility by
supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for
several response types. We further showcase how to construct ensembles of these models,
evaluate models using inbuilt cross-validation, and use other convenience functions for
DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches
to regression with non-tabular data

Lucas Kook

https://www.jstatsoft.org/article/view/v111i10

Vienna University of
Economics and Business

November 2024

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