{ "_id": "6a1e9c611d7bb097a0a6ecb3", "Package": "BayesRTMB", "Type": "Package", "Title": "Bayesian Inference Using 'RTMB'", "Version": "0.1.1", "Authors@R": "person(\"Hiroshi\", \"Shimizu\", email = \"simizu706@gmail.com\", role = c(\"aut\", \"cre\"))", "Description": "Provides tools for Markov chain Monte Carlo (MCMC) and\nMaximum A Posteriori (MAP) estimation utilizing the 'RTMB'\npackage. It supports various statistical models including\ngeneralized linear mixed models, factor analysis, item response\ntheory, and multidimensional unfolding. The package allows\nusers to easily transition between frequentist and Bayesian\nparadigms using a unified interface. Automatic differentiation\nand Laplace approximation follow Kristensen et al. (2016)\n, and MCMC sampling uses the\nNo-U-Turn Sampler described by Hoffman and Gelman (2014)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "URL": "https://github.com/norimune/BayesRTMB,\nhttps://norimune.github.io/BayesRTMB/", "BugReports": "https://github.com/norimune/BayesRTMB/issues", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "RoxygenNote": "7.3.3", "Repository": "https://norimune.r-universe.dev", "Date/Publication": "2026-05-28 07:17:29 UTC", "RemoteUrl": "https://github.com/norimune/bayesrtmb", "RemoteRef": "HEAD", "RemoteSha": "1baeafa3b43e993fb8e221056728524510e1eef4", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-02 08:58:47 UTC", "User": "root" }, "Author": "Hiroshi Shimizu [aut, cre]", "Maintainer": "Hiroshi Shimizu ", "MD5sum": "a6156210dfc0207c1e20db3756f6266c", "_user": "norimune", "_type": "src", "_file": "BayesRTMB_0.1.1.tar.gz", "_fileid": 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Inference (ADVI)", "topics": [ "ADVI_method" ] }, { "page": "bayes_factor", "title": "Calculate Bayes Factor", "topics": [ "bayes_factor" ] }, { "page": "beverage", "title": "Beverage Preference Data", "topics": [ "beverage" ] }, { "page": "BigFive", "title": "Big Five Personality Traits Data", "topics": [ "BigFive" ] }, { "page": "Classic_Fit", "title": "Classic fit object", "topics": [ "Classic_Fit" ] }, { "page": "conditional_effects", "title": "Calculate Conditional Effects", "topics": [ "conditional_effects" ] }, { "page": "conditional_effects.mcmc_fit", "title": "Calculate conditional effects for MCMC fit objects", "topics": [ "conditional_effects.mcmc_fit" ] }, { "page": "debate", "title": "Debate Simulation Data", "topics": [ "debate" ] }, { "page": "Dim", "title": "Define parameter dimensions and types", "topics": [ "Dim" ] }, { "page": "distance", "title": "Euclidean distance", "topics": [ "distance" ] }, { "page": "distributions", "title": "Probability Distributions for RTMB 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posterior densities for MCMC samples", "topics": [ "plot_dens" ] }, { "page": "plot_forest", "title": "Plot parameter estimates and credible intervals (Forest Plot)", "topics": [ "plot_forest" ] }, { "page": "plot_item_curve", "title": "Plot item/category response curves", "topics": [ "plot_item_curve" ] }, { "page": "plot_item_info", "title": "Plot item information functions", "topics": [ "plot_item_info" ] }, { "page": "plot_lsmeans", "title": "Plot least-squares marginal means", "topics": [ "plot_lsmeans" ] }, { "page": "plot_mdu", "title": "Plot Multidimensional Unfolding Configuration", "topics": [ "plot_mdu" ] }, { "page": "plot_pairs", "title": "Plot pairs for posterior samples", "topics": [ "plot_pairs" ] }, { "page": "plot_test_info", "title": "Plot test information function", "topics": [ "plot_test_info" ] }, { "page": "plot_trace", "title": "Plot MCMC trace plots", "topics": [ "plot_trace" ] }, { "page": "plot.ce_rtmb", "title": "Plot method for ce_rtmb class (Base R)", 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"Specify normal/exponential priors for MAP and Bayesian inference", "topics": [ "prior_normal" ] }, { "page": "prior_rhs", "title": "Specify a Regularized Horseshoe prior for continuous shrinkage", "topics": [ "prior_rhs" ] }, { "page": "prior_ssp", "title": "Specify a Spike-and-Slab prior for variable selection", "topics": [ "prior_ssp" ] }, { "page": "prior_uniform", "title": "Specify a flat prior", "topics": [ "prior_uniform" ] }, { "page": "prior_weak", "title": "Specify a weakly informative prior", "topics": [ "prior_weak" ] }, { "page": "quad_form_chol", "title": "Quadratic form using a Cholesky factor", "topics": [ "quad_form_chol" ] }, { "page": "quad_form_diag", "title": "Quadratic form with a diagonal matrix", "topics": [ "quad_form_diag" ] }, { "page": "quantile95", "title": "Calculate 95% Quantiles", "topics": [ "quantile95" ] }, { "page": "r_hat", "title": "Calculate Rank-normalized Split-R-hat", "topics": [ "r_hat" ] }, { "page": "read_mcmc_csv", "title": "Restore MCMC Fit from CSV", "topics": [ "read_mcmc_csv" ] }, { "page": "rtmb_code", "title": "Define an RTMB Model with Stan-like Syntax", "topics": [ "rtmb_code" ] }, { "page": "rtmb_corr", "title": "Fit a Correlation Model using RTMB", "topics": [ "rtmb_corr" ] }, { "page": "rtmb_fa", "title": "RTMB-based Factor Analysis Wrapper", "topics": [ "rtmb_fa" ] }, { "page": "RTMB_Fit_Base", "title": "Base class for RTMB Fit objects", "topics": [ "RTMB_Fit_Base" ] }, { "page": "rtmb_glm", "title": "RTMB-based GLM wrapper function (no random effects)", "topics": [ "rtmb_glm" ] }, { "page": "rtmb_glmer", "title": "RTMB-based GLMM wrapper function", "topics": [ "rtmb_glmer" ] }, { "page": "rtmb_irt", "title": "RTMB-based IRT (Item Response Theory) Wrapper", "topics": [ "rtmb_irt" ] }, { "page": "rtmb_lm", "title": "RTMB-based Linear Regression wrapper function", "topics": [ "rtmb_lm" ] }, { "page": "rtmb_lmer", "title": "RTMB-based Linear Mixed Model (LMM) wrapper function", "topics": [ "rtmb_lmer" ] }, { 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"rtmb_ttest", "title": "RTMB-based Bayesian two-sample t-test wrapper function", "topics": [ "rtmb_ttest" ] }, { "page": "rtmb_wrappers", "title": "Common Features and Arguments of RTMB Wrapper Functions", "concept": [ "wrappers" ], "topics": [ "rtmb_wrappers" ] }, { "page": "safe_rtmb_model", "title": "Safe RTMB model construction (with error message translation)", "topics": [ "safe_rtmb_model" ] }, { "page": "simple_effects", "title": "Calculate Simple Effects", "topics": [ "simple_effects" ] }, { "page": "simple_effects.mcmc_fit", "title": "Simple effects for MCMC fit objects", "topics": [ "simple_effects.mcmc_fit" ] }, { "page": "softmax", "title": "Softmax function", "topics": [ "softmax" ] }, { "page": "sort_loadings", "title": "Sort and display factor loadings neatly", "topics": [ "sort_loadings" ] }, { "page": "squared_distance", "title": "Squared Euclidean distance", "topics": [ "squared_distance" ] }, { "page": "stz_basis", "title": "stz basis function", "topics": [ "stz_basis" ] }, { "page": "sum_to_zero", "title": "Sum-to-zero transformation", "topics": [ "sum_to_zero" ] }, { "page": "summary.ce_rtmb", "title": "Summary method for ce_rtmb class", "topics": [ "summary.ce_rtmb" ] }, { "page": "test_info", "title": "Calculate Test Information Function", "topics": [ "test_info" ] }, { "page": "to_centered_matrix", "title": "Vector to centered matrix (RTMB compatible)", "topics": [ "to_centered_matrix" ] }, { "page": "to_centered_tri", "title": "Vector to centered triangular matrix (RTMB compatible)", "topics": [ "to_centered_tri" ] }, { "page": "to_long", "title": "Convert Wide Data to Long Format", "topics": [ "to_long" ] }, { "page": "to_lower_tri", "title": "Vector to lower triangular matrix (RTMB compatible)", "topics": [ "to_lower_tri" ] }, { "page": "to_wide", "title": "Convert Long Data to Wide Format", "topics": [ "to_wide" ] }, { "page": "training", "title": "Social Skills Training Data", "topics": [ "training" ] }, { "page": 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