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 loglikelihood 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, and easy to use. This paper presents the architecture and performance of the package, details its use, and presents some results using ...
Mixed Logit model (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal...
Developments in simulation methods, and the computational power that is now available, have enabled ...
This paper introduces a variant of random utility choice models based on mixed probability density f...
This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial log...
mlogit is a package for R which enables the estimation of the multinomial logit models with individu...
We present a summary of important computational issues and opportunities that arise from the use of ...
This paper describes the (post)estimation framework and folder structure to ensure an efficient work...
Rchoice is a package in R for estimating models with individual heterogeneity for both cross-section...
Mixed Logit is an advanced and flexible tool for the study of discrete choice problems. However, thi...
This paper demonstrates a method for estimating logit choice models for small sample data, including...
Description Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a n...
In Alberini et al. (this volume), an overview of the workhorse model (the Multinomial Logit, or MNL)...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/15...
This paper introduces a variant of random utility choice models based on mixed probability density f...
In this thesis an algorithm (MLOPT) for mixed logit models is proposed. Mixed logit models are flexi...
Mixed Logit model (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal...
Developments in simulation methods, and the computational power that is now available, have enabled ...
This paper introduces a variant of random utility choice models based on mixed probability density f...
This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial log...
mlogit is a package for R which enables the estimation of the multinomial logit models with individu...
We present a summary of important computational issues and opportunities that arise from the use of ...
This paper describes the (post)estimation framework and folder structure to ensure an efficient work...
Rchoice is a package in R for estimating models with individual heterogeneity for both cross-section...
Mixed Logit is an advanced and flexible tool for the study of discrete choice problems. However, thi...
This paper demonstrates a method for estimating logit choice models for small sample data, including...
Description Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a n...
In Alberini et al. (this volume), an overview of the workhorse model (the Multinomial Logit, or MNL)...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/15...
This paper introduces a variant of random utility choice models based on mixed probability density f...
In this thesis an algorithm (MLOPT) for mixed logit models is proposed. Mixed logit models are flexi...
Mixed Logit model (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal...
Developments in simulation methods, and the computational power that is now available, have enabled ...
This paper introduces a variant of random utility choice models based on mixed probability density f...