mixl: An open-source R package for estimating complex choice models on large datasets

Joseph Molloy, Felix Becker, Basil Schmid and Kay W. Axhausen

Journal of choice modelling, 2021, vol. 39, issue C

Abstract: This paper introduces mixl, a new R package for the estimation of advanced choice models. The estimation of such models typically relies on simulation methods with a large number of random draws to obtain stable results. mixl uses inherent properties of the log-likelihood problem structure to greatly reduce both the memory usage and runtime of the estimation procedure for specific types of mixed multinomial logit models. Functions for prediction and posterior analysis are included. Parallel computing is also supported, with near linear speedups observed on up to 24 cores. mixl is directly accessible from R, available on CRAN. We show that mixl is fast, easy to use, and scales to very large datasets. This paper presents the architecture and performance of the package, details its use, and presents some results using real world data and models.

Keywords: Multinomial logit; Mixed logit; Choice modelling; R, hybrid choice; Estimation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:39:y:2021:i:c:s1755534521000178

DOI: 10.1016/j.jocm.2021.100284

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