mixl: Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets

Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.

Version:	1.3.3
Imports:	maxLik, numDeriv, randtoolbox, Rcpp (≥ 0.12.19), readr, sandwich, stats, stringr (≥ 1.3.1)
Suggests:	knitr, mlogit, rmarkdown, testthat, texreg, xtable
Published:	2021-12-08
Author:	Joseph Molloy [aut, cre]
Maintainer:	Joseph Molloy <joseph.molloy at ivt.baug.ethz.ch>
BugReports:	https://github.com/joemolloy/fast-mixed-mnl/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/joemolloy/fast-mixed-mnl
NeedsCompilation:	yes
CRAN checks:	mixl results

Documentation:

Reference manual:	mixl.pdf
Vignettes:	"Mixl User Guide"

Downloads:

Package source:	mixl_1.3.3.tar.gz
Windows binaries:	r-devel: mixl_1.3.3.zip, r-release: mixl_1.3.3.zip, r-oldrel: mixl_1.3.3.zip
macOS binaries:	r-release (arm64): mixl_1.3.3.tgz, r-oldrel (arm64): mixl_1.3.3.tgz, r-release (x86_64): mixl_1.3.3.tgz, r-oldrel (x86_64): mixl_1.3.3.tgz
Old sources:	mixl archive

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