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TorchDCM package offers faster discrete choice modeling with PyTorch

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TorchDCM, a new open-source Python package designed to streamline discrete choice modeling (DCM) using PyTorch. This package aims to bridge the gap between traditional econometric workflows and scalable, differentiable computation, particularly for large and simulation-intensive models. TorchDCM offers comprehensive econometric functionality, supports various likelihoods and model specifications, and demonstrates significant speed improvements over existing software, especially when utilizing CUDA devices. AI

IMPACT Accelerates research and development in econometric modeling by providing a faster, more scalable computational framework.

RANK_REASON The cluster describes a new open-source package for discrete choice modeling published as an arXiv paper. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TorchDCM package offers faster discrete choice modeling with PyTorch

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The cluster describes a new open-source package for discrete choice modeling published as an arXiv paper. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Baichuan Mo, Zhengzhong Ricky You, Xiqun Michael Chen, Ruimin Li ·

    TorchDCM: A Unified PyTorch-Native Package for Discrete Choice Modeling

    arXiv:2608.19231v1 Announce Type: cross Abstract: Estimating large and simulation-intensive discrete choice models (DCMs) requires repeated evaluation of utilities, probabilities, derivatives, and simulated likelihoods over many observations, alternatives, and draws. Existing DCM…

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