Package: mixl 1.3.5.9000

Daniel Heimgartner

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. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.

Authors:Joseph Molloy [aut], Daniel Heimgartner [ctb, cre]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mixl/json (API)

# Install 'mixl' in R:

install.packages('mixl', repos = c('https://joemolloy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/joemolloy/fast-mixed-mnl/issues

On CRAN:

6.08 score 6 stars 1 packages 9 scripts 4.5k downloads 12 exports 37 dependencies

Last updated from:f132ff2b65. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	142
linux-devel-x86_64	OK	137
source / vignettes	OK	175
linux-release-arm64	OK	132
linux-release-x86_64	OK	138
macos-release-arm64	OK	137
macos-release-x86_64	OK	216
macos-oldrel-arm64	OK	122
macos-oldrel-x86_64	OK	272
windows-devel	OK	95
windows-release	OK	89
windows-oldrel	OK	89
wasm-release	OK	112

Exports:av_matrix compileUtilityFunction create_halton_draws estimate extract_av_cols extract_indiv_data generate_default_availabilities posteriors probabilities specify_model summary_tex utilities

Dependencies:bit bit64 cli clipr cpp11 crayon digest generics glue hms lattice lifecycle magrittr maxLik miscTools numDeriv pillar pkgconfig prettyunits progress R6 randtoolbox Rcpp readr rlang rngWELL sandwich stringi stringr tibble tidyselect tzdb utf8 vctrs vroom withr zoo

Mixl User Guide

Rendered fromuser-guide.Rmdusingknitr::rmarkdown

Last update: 2025-05-10
Started: 2019-02-14

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Estimate mixed multinomial logit models	mixl-package mixl
Extract the availabilites matrix from the dataset, using column indicies	av_matrix
Check the inputs to the draw function	check_draw_inputs
Check the inputs to the estimate function	check_inputs
compileUtilityFunction Deprecated, please see 'specify_model()'	compileUtilityFunction
Create a standard set of Halton draws to use in estimation	create_halton_draws
Runs a maximum likelihood estimation on a mixl choice model	estimate
Extract the availabilites matrix from the dataset using a column name prefix	extract_av_cols
Extract the individual level data from the dataset for use in posterior analysis	extract_indiv_data
Generate a ones-matrix of availabilities	generate_default_availabilities
Calculate the posteriors for a specified and estimated model	posteriors
Prints the output of a model	print.mixl
Print a model summary	print.summary.mixl
Calculate the probabilities for a specified and estimated model. Note that if new data or draws are provided, the model will not be re-estimated	probabilities
Validate the utility functions against the dataset and generate the optimised logliklihood function	specify_model
Return tex formatted output of a model summary. If an output_file parameter is provided, save the object to that location	summary_tex
Create a model summary	summary.mixl
Return the the utilities for a set of coefficients	utilities
Calculates the Variance-Covariance Matrix of the mixl summary	vcov.mixl