logitr

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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:

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).

Basic Usage

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

JSS Article

An associated paper in the Journal of Statistical Software about this package is available at https://doi.org/10.18637/jss.v105.i10

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)

Author, Version, and License Information

Citation Information

If you use this package for in a publication, please cite the JSS article associated with it! You can get the citation by typing citation("logitr") into R:

citation("logitr")
#> 
#> To cite logitr in publications use:
#> 
#>   Helveston JP (2023). "logitr: Fast Estimation of Multinomial and
#>   Mixed Logit Models with Preference Space and Willingness-to-Pay Space
#>   Utility Parameterizations." _Journal of Statistical Software_,
#>   *105*(10), 1-37. doi:10.18637/jss.v105.i10
#>   <https://doi.org/10.18637/jss.v105.i10>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{logitr}: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations},
#>     author = {John Paul Helveston},
#>     journal = {Journal of Statistical Software},
#>     year = {2023},
#>     volume = {105},
#>     number = {10},
#>     pages = {1--37},
#>     doi = {10.18637/jss.v105.i10},
#>   }