logitr

logitr: Fast Estimation of Multinomial (MNL) and Mixed Logit (MXL) Models with Preference Space and Willingness to Pay Space Utility Parameterizations

The latest version includes support for:

• Multinomial logit (MNL) models
• Mixed logit (MXL) models with normal and log-normal parameter distributions.
• Preference space and WTP space utility parameterizations.
• Weighted models to differentially weight individual observations.
• Uncorrelated or correlated heterogeneity covariances for mixed logit models.
• Functions for computing WTP from preference space models.
• Functions for predicting expected probabilities and outcomes for sets of alternatives based on an estimated model.
• A parallelized multistart optimization loop that uses different random starting points in each iteration to search for different local minima (useful for non-convex problems like MXL models or models with WTP space parameterizations).

Mixed logit models are estimated using maximum simulated likelihood based on the algorithms in Kenneth Train’s book Discrete Choice Methods with Simulation, 2nd Edition (New York: Cambridge University Press, 2009).

Installation

You can install {logitr} from CRAN:

install.packages("logitr")

or you can install the development version of {logitr} from GitHub:

# install.packages("remotes")
remotes::install_github("jhelvy/logitr")

Load the library with:

library(logitr)

Basic Usage

View the basic usage page for details on how to use logitr to estimate models.

Author, Version, and License Information

• Author: John Paul Helveston https://www.jhelvy.com/
• Date First Written: Sunday, September 28, 2014
• License: MIT

Citation Information

If you use this package for in a publication, I would greatly appreciate it if you cited it - you can get the citation by typing citation("logitr") into R:

citation("logitr")
#> 
#> To cite logitr in publications use:
#> 
#>   John Paul Helveston (2022). logitr: Fast Estimation of Multinomial
#>   and Mixed Logit Models with Preference Space and Willingness to Pay
#>   Space Utility Parameterizations.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness to Pay Space Utility Parameterizations},
#>     author = {John Paul Helveston},
#>     year = {2022},
#>     note = {R package},
#>     url = {https://jhelvy.github.io/logitr/},
#>   }