logitr: Logit Models w/Preference & WTP Space Utility Parameterizations

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the 'nloptr' package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. MXL models assume uncorrelated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) "Discrete Choice Methods with Simulation, 2nd Edition" <doi:10.1017/CBO9780511805271>.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: nloptr, stats, randtoolbox, MASS
Suggests: dplyr, fastDummies, knitr, rmarkdown, here, ggplot2, testthat
Published: 2021-10-25
Author: John Helveston ORCID iD [aut, cre, cph], Connor Forsythe [ctb]
Maintainer: John Helveston <john.helveston at gmail.com>
BugReports: https://github.com/jhelvy/logitr/issues
License: MIT + file LICENSE
URL: https://github.com/jhelvy/logitr
NeedsCompilation: no
Citation: logitr citation info
Materials: README NEWS
CRAN checks: logitr results

Documentation:

Reference manual: logitr.pdf
Vignettes: Basic Usage
Data Formatting and Encoding
Estimating Models with Interactions
Estimating Multinomial Logit Models
Estimating Weighted Logit Models
Estimating Mixed Logit Models
Predicting Probabilities and Outcomes with Estimated Models
Utility Models in the Preference & WTP Space

Downloads:

Package source: logitr_0.4.0.tar.gz
Windows binaries: r-devel: logitr_0.3.0.zip, r-release: logitr_0.3.0.zip, r-oldrel: logitr_0.3.0.zip
macOS binaries: r-release (arm64): logitr_0.3.0.tgz, r-release (x86_64): logitr_0.3.0.tgz, r-oldrel: logitr_0.3.0.tgz
Old sources: logitr archive

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