The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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.
Version: | 1.3.1 |
Depends: | R (≥ 3.5.0) |
Suggests: | bayesm, MASS, lattice, Matrix, testthat (≥ 3.0.0) |
Published: | 2024-10-10 |
DOI: | 10.32614/CRAN.package.ChoiceModelR |
Author: | Ryan Sermas [aut], John V Colias [ctb, cre], Decision Analyst, Inc. [cph] |
Maintainer: | John V Colias <jcolias at decisionanalyst.com> |
License: | GPL (≥ 3) |
Copyright: | Copyright (C) 2012 Decision Analyst, Inc.; 604 Avenue H East, Arlington, Texas 76011; www.decisionanalyst.com; 817-640-6166 (ChoiceModelR is a trademark of Decision Analyst, Inc.) |
URL: | https://www.decisionanalyst.com/ |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | ChoiceModelR results |
Reference manual: | ChoiceModelR.pdf |
Package source: | ChoiceModelR_1.3.1.tar.gz |
Windows binaries: | r-devel: ChoiceModelR_1.3.1.zip, r-release: ChoiceModelR_1.3.1.zip, r-oldrel: ChoiceModelR_1.3.1.zip |
macOS binaries: | r-release (arm64): ChoiceModelR_1.3.1.tgz, r-oldrel (arm64): ChoiceModelR_1.3.1.tgz, r-release (x86_64): ChoiceModelR_1.3.1.tgz, r-oldrel (x86_64): ChoiceModelR_1.3.1.tgz |
Old sources: | ChoiceModelR archive |
Please use the canonical form https://CRAN.R-project.org/package=ChoiceModelR to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.