Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application

Abstract The community of choice modellers has expanded substantially over recent years, covering many disciplines and encompassing users with very different levels of econometric and computational skills. This paper presents an introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users. Apollo also incorporates numerous post-estimation tools, allows for both classical and Bayesian estimation, and permits advanced users to develop their own routines for new model structures.

[1]  F. Koppelman,et al.  Alternative nested logit models: structure, properties and estimation , 1998 .

[2]  H. Williams On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit , 1977 .

[3]  Emily Lancsar,et al.  Best worst discrete choice experiments in health: methods and an application. , 2013, Social science & medicine.

[4]  Stephane Hess,et al.  Recovery of inter- and intra-personal heterogeneity using mixed logit models , 2011 .

[5]  Moshe Ben-Akiva,et al.  Hybrid choice models , 2014 .

[6]  Dirk Eddelbuettel,et al.  Rcpp: Seamless R and C++ Integration , 2011 .

[7]  Sanford Weisberg Computing Primer for Applied Linear Regression, Third Edition , 2005 .

[8]  Stephane Hess,et al.  On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice , 2006 .

[9]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[10]  Michel Bierlaire,et al.  BIOGEME: a free package for the estimation of discrete choice models , 2003 .

[11]  I. Krinsky,et al.  On Approximating the Statistical Properties of Elasticities , 1986 .

[12]  Michel Bierlaire,et al.  A Heuristic for Nonlinear Global Optimization , 2010, INFORMS J. Comput..

[13]  Andrew Daly,et al.  Calculating Errors for Measures Derived from Choice Modeling Estimates , 2012 .

[14]  Arne Henningsen,et al.  maxLik: A package for maximum likelihood estimation in R , 2011, Comput. Stat..

[15]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[16]  Stephane Hess,et al.  An accumulation of preference: Two alternative dynamic models for understanding transport choices , 2021, Transportation Research Part B: Methodological.

[17]  Stefan Lindhard Mabit,et al.  Easy and flexible mixture distributions , 2013 .

[18]  A. Daly,et al.  Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour , 2012 .

[19]  Jeff Dumont,et al.  Functions for Hierarchical Bayesian Estimation: A FlexibleApproach , 2015 .

[20]  J. Halton On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals , 1960 .

[21]  S. Hess,et al.  Understanding the formation and influence of attitudes in patients' treatment choices for lower back pain: testing the benefits of a hybrid choice model approach. , 2014, Social science & medicine.

[22]  David A. Hensher,et al.  Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model , 2010 .

[23]  Daniel McFadden,et al.  Modelling the Choice of Residential Location , 1977 .

[24]  C. Bhat The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions , 2008 .

[25]  J. Townsend,et al.  Fundamental derivations from decision field theory , 1992 .

[26]  Joan L. Walker,et al.  How, when and why integrated choice and latent variable models are latently useful , 2016 .

[27]  A. Daly,et al.  Handbook of Choice Modelling , 2014 .

[28]  Chandra R. Bhat,et al.  A Multiple Discrete-Continuous Nested Extreme Value (MDCNEV) Model: Formulation and Application to Non-worker Activity Time-Use and Timing Behavior on Weekdays , 2010 .

[29]  Stephane Hess,et al.  Advanced discrete choice models with applications to transport demand , 2005 .

[30]  Andrew Daly,et al.  Estimating “tree” logit models , 1987 .

[31]  K. Train Discrete Choice Methods with Simulation , 2003 .

[32]  Stephane Hess,et al.  Latent class structures: taste heterogeneity and beyond , 2014 .

[33]  John Geweke,et al.  Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .

[34]  T. Hassard,et al.  Applied Linear Regression , 2005 .

[35]  Peter Vovsha,et al.  Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area , 1997 .

[36]  F. Koppelman,et al.  The generalized nested logit model , 2001 .

[37]  Turalay Kenc,et al.  Ox: An Object-Oriented Matrix Language , 1997 .

[38]  Palma Araneda,et al.  Modelling wine consumer preferences using hybrid choice models : inclusion of intrinsic and extrinsic attributes , 2016 .