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RprobitB
View on CRAN: Click
here
Download and install RprobitB package within the R console
Install from CRAN:
install.packages("RprobitB")
Install from Github:
library("remotes")
install_github("cran/RprobitB")
Install by package version:
library("remotes")
install_version("RprobitB", "1.1.4")
Attach the package and use:
library("RprobitB")
Maintained by
Lennart Oelschläger
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.RprobitB . https://github.com/loelschlaeger/RprobitB/issues . https://loelschlaeger.de/RprobitB/ . https://github.com/loelschlaeger/RprobitB . RprobitB results . RprobitB.pdf . RprobitB_1.1.4.tar.gz . RprobitB_1.1.4.zip . RprobitB_1.1.4.zip . RprobitB_1.1.4.zip . RprobitB_1.1.4.tgz . RprobitB_1.1.4.tgz . RprobitB_1.1.4.tgz . RprobitB_1.1.4.tgz . RprobitB archive . https://CRAN.R-project.org/package=RprobitB .
First Published: 2021-05-15
Latest Update: 2022-11-06
Description:
Bayes estimation of probit choice models, both in the cross-sectional and
panel setting. The package can analyze binary, multivariate, ordered, and
ranked choices, as well as heterogeneity of choice behavior among deciders.
The main functionality includes model fitting via Markov chain Monte Carlo m
ethods, tools for convergence diagnostic, choice data simulation, in-sample
and out-of-sample choice prediction, and model selection using information
criteria and Bayes factors. The latent class model extension facilitates
preference-based decider classification, where the number of latent classes
can be inferred via the Dirichlet process or a weight-based updating
heuristic. This allows for flexible modeling of choice behavior without the
need to impose structural constraints.
For a reference on the method see Oelschlaeger and Bauer (2021)
.
How to cite:
Lennart Oelschläger (2021). RprobitB: Bayesian Probit Choice Modeling. R package version 1.1.4, https://cran.r-project.org/web/packages/RprobitB. Accessed 16 Apr. 2025.
Previous versions and publish date:
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Complete documentation for RprobitB
Functions, R codes and Examples using
the RprobitB R package
Some associated functions: M . R_hat . RprobitB . RprobitB_data . RprobitB_fit . RprobitB_gibbs_samples_statistics . RprobitB_latent_classes . RprobitB_normalization . RprobitB_parameter . WAIC . as_cov_names . check_form . check_prior . choice_berserk . choice_probabilities . classification . coef.RprobitB_fit . compute_choice_probabilities . compute_p_si . cov_mix . create_labels_Omega . create_labels_Sigma . create_labels_alpha . create_labels_b . create_labels_d . create_labels_s . create_lagged_cov . d_to_gamma . delta . dmvnorm . draw_from_prior . euc_dist . filter_gibbs_samples . fit_model . get_cov . gibbs_sampling . is_covariance_matrix . ll_ordered . missing_covariates . mml . model_selection . npar . overview_effects . parameter_labels . permutations . plot.RprobitB_data . plot.RprobitB_fit . plot_acf . plot_class_allocation . plot_mixture_contour . plot_mixture_marginal . plot_roc . point_estimates . posterior_pars . pprint . pred_acc . predict.RprobitB_fit . preference_flip . prepare_data . rdirichlet . rmvnorm . rtnorm . rttnorm . rwishart . set_initial_gibbs_values . simulate_choices . sufficient_statistics . train_test . transform . transform_gibbs_samples . transform_parameter . undiff_Sigma . update.RprobitB_fit . update_Omega . update_Sigma . update_U . update_U_ranked . update_b . update_classes_dp . update_classes_wb . update_d . update_m . update_reg . update_s . update_z .
Some associated R codes: RcppExports.R . RprobitB_package.R . data_management.R . datasets.R . model_evaluation.R . model_fitting.R . model_selection.R . plot.R . utils.R . Full RprobitB package functions and examples
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