Package: ChoiceModelR 1.3.1

ChoiceModelR: Choice Modeling in R

Implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

Authors:Ryan Sermas [aut], John V Colias [ctb, cre], Decision Analyst, Inc. [cph]

ChoiceModelR_1.3.1.tar.gz
ChoiceModelR_1.3.1.zip(r-4.7-any)ChoiceModelR_1.3.1.zip(r-4.6-any)ChoiceModelR_1.3.1.zip(r-4.5-any)
ChoiceModelR_1.3.1.tgz(r-4.6-any)ChoiceModelR_1.3.1.tgz(r-4.5-any)
ChoiceModelR_1.3.1.tar.gz(r-4.7-any)ChoiceModelR_1.3.1.tar.gz(r-4.6-any)
ChoiceModelR_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ChoiceModelR/json (API)

# Install 'ChoiceModelR' in R:
install.packages('ChoiceModelR', repos = c('https://jvcolias.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • datar - Arificial (Simulated) Choice Data for choicemodelr
  • sharedatar - Arificial (Simulated) Fractional Choice Data for choicemodelr
  • truebetas - True betas used to simulate data in the choice data set named datar, which is used in the example.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

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Last updated from:e1aac557a0. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK142
source / vignettesOK181
linux-releaseOK104
macos-releaseOK128
macos-oldrelOK156
windows-develOK65
windows-releaseOK109
windows-oldrelOK83
wasm-releaseOK80

Exports:choicemodelr

Dependencies: