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multinomineq  

Bayesian Inference for Multinomial Models with Inequality Constraints
View on CRAN: Click here


Download and install multinomineq package within the R console
Install from CRAN:
install.packages("multinomineq")

Install from Github:
library("remotes")
install_github("cran/multinomineq")

Install by package version:
library("remotes")
install_version("multinomineq", "0.2.6")



Attach the package and use:
library("multinomineq")
Maintained by
Daniel W. Heck
[Scholar Profile | Author Map]
First Published: 2019-05-16
Latest Update: 2024-02-20
Description:
Implements Gibbs sampling and Bayes factors for multinomial models with linear inequality constraints on the vector of probability parameters. As special cases, the model class includes models that predict a linear order of binomial probabilities (e.g., p[1] < p[2] < p[3] < .50) and mixture models assuming that the parameter vector p must be inside the convex hull of a finite number of predicted patterns (i.e., vertices). A formal definition of inequality-constrained multinomial models and the implemented computational methods is provided in: Heck, D.W., & Davis-Stober, C.P. (2019). Multinomial models with linear inequality constraints: Overview and improvements of computational methods for Bayesian inference. Journal of Mathematical Psychology, 91, 70-87. . Inequality-constrained multinomial models have applications in the area of judgment and decision making to fit and test random utility models (Regenwetter, M., Dana, J., & Davis-Stober, C.P. (2011). Transitivity of preferences. Psychological Review, 118, 42
How to cite:
Daniel W. Heck (2019). multinomineq: Bayesian Inference for Multinomial Models with Inequality Constraints. R package version 0.2.6, https://cran.r-project.org/web/packages/multinomineq. Accessed 03 May. 2025.
Previous versions and publish date:
0.2.1 (2019-05-16 18:30), 0.2.3 (2022-02-28 14:40), 0.2.4 (2022-08-22 01:40), 0.2.5 (2022-11-22 10:10)
Other packages that cited multinomineq R package
View multinomineq citation profile
Other R packages that multinomineq depends, imports, suggests or enhances
Complete documentation for multinomineq
Functions, R codes and Examples using the multinomineq R package
Some associated functions: Ab_drop_fixed . Ab_max . Ab_multinom . Ab_sort . V_to_Ab . bf_binom . bf_equality . bf_nonlinear . binom_to_multinom . count_binom . count_multinom . count_to_bf . drop_fixed . find_inside . heck2017 . heck2017_raw . hilbig2014 . inside . inside_binom . karabatsos2004 . ml_binom . model_weights . multinomineq-package . nirt_to_Ab . population_bf . postprob . ppp_binom . regenwetter2012 . rpbinom . rpdirichlet . sampling_multinom . sampling_nonlinear . stochdom_Ab . stochdom_bf . strategy_marginal . strategy_multiattribute . strategy_postprob . strategy_to_Ab . strategy_unique . swop5 . 
Some associated R codes: Ab_max.R . RcppExports.R . auxiliary.R . bf_binom.R . bf_equality.R . bf_nonlinear.R . checks.R . classes_ineq.R . conjoint_measurement.R . count_binomial.R . count_multinomial.R . count_nonconvex.R . count_to.R . data_hilbig2014_heck2017.R . data_karabatsos2004.R . data_regenwetter2012.R . data_swop.R . equality_constraints.R . find_inside.R . inside.R . line_clipping.R . ml_estimates.R . model_weights.R . multinomineq_package.R . nirt.R . population_bf.R . posterior_predictive.R . postprob.R . rpmultinom.R . run_parallel.R . sampling_nonlinear.R . sampling_polytope.R . sampling_vertex.R . sort_inequalities.R . stochdom_Ab.R . stochdom_bf.R . strategy_marginal.R . strategy_multiattribute.R . strategy_unique.R . transform_Ab_V.R .  Full multinomineq package functions and examples
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