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RprobitB  

Bayesian Probit Choice Modeling
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]
All associated links for this package
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 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2021-05-15 14:00), 0.1.1 (2021-05-25 10:10), 1.0.0 (2021-11-12 17:50), 1.1.0 (2022-07-22 13:00), 1.1.1 (2022-08-11 16:10), 1.1.2 (2022-11-06 18:40), 1.1.3 (2024-02-08 09:20)
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Other R packages that RprobitB depends, imports, suggests or enhances
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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