mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods

TL;DR

The paper introduces the R package mixdistreg, enabling flexible mixture of experts distributional regression modeling using neural structured additive learning principles with TensorFlow integration.

Contribution

It provides a high-level R package that unifies various mixture of experts distributional regression approaches within a neural structured additive learning framework.

Findings

Demonstrates the package's functionality through multiple code examples

Integrates mixture density networks and regression approaches in a unified framework

Facilitates flexible distributional modeling in R using deep learning techniques

Abstract

This paper presents a high-level description of the R software package mixdistreg to fit mixture of experts distributional regression models. The proposed framework is implemented in R using the deepregression software template, which is based on TensorFlow and follows the neural structured additive learning principle. The software comprises various approaches as special cases, including mixture density networks and mixture regression approaches. Various code examples are given to demonstrate the package's functionality.