Package: mixl 1.3.3

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>.

Authors:Joseph Molloy [aut, cre]

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mixl/json (API)

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

Peer review:

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

On CRAN:

4.82 score 4 stars 8 scripts 1.1k downloads 12 exports 38 dependencies

Last updated 12 months agofrom:a5bb91c083. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 08 2024
R-4.5-win-x86_64NOTEOct 08 2024
R-4.5-linux-x86_64NOTEOct 08 2024
R-4.4-win-x86_64NOTEOct 08 2024
R-4.4-mac-x86_64NOTEOct 08 2024
R-4.4-mac-aarch64NOTEOct 08 2024
R-4.3-win-x86_64OKOct 08 2024
R-4.3-mac-x86_64OKOct 08 2024
R-4.3-mac-aarch64OKOct 08 2024

Exports:av_matrixcompileUtilityFunctioncreate_halton_drawsestimateextract_av_colsextract_indiv_datagenerate_default_availabilitiesposteriorsprobabilitiesspecify_modelsummary_texutilities

Dependencies:bitbit64clicliprcpp11crayondigestfansigenericsgluehmslatticelifecyclemagrittrmaxLikmiscToolsnumDerivpillarpkgconfigprettyunitsprogressR6randtoolboxRcppreadrrlangrngWELLsandwichstringistringrtibbletidyselecttzdbutf8vctrsvroomwithrzoo

Mixl User Guide

Rendered fromuser-guide.Rmdusingknitr::rmarkdownon Oct 08 2024.

Last update: 2021-02-26
Started: 2019-02-14