{ "_id": "6a63418fb715785e4da9c6c5", "Package": "BayesRTMB", "Type": "Package", "Title": "Bayesian Inference Using 'RTMB'", "Version": "0.2.4", "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. 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"item_info.RTMB_Fit_Base", "title": "Item Information Function for RTMB_Fit_Base", "topics": [ "item_info.RTMB_Fit_Base" ] }, { "page": "log_det_chol", "title": "Log determinant of a Cholesky factor", "topics": [ "log_det_chol" ] }, { "page": "log_mix", "title": "Log mixture of two probabilities", "topics": [ "log_mix" ] }, { "page": "log_softmax", "title": "Log-softmax function", "topics": [ "log_softmax" ] }, { "page": "log_sum_exp", "title": "Log-sum-exp function", "topics": [ "log_sum_exp" ] }, { "page": "log_sum_exp_matrix", "title": "Log-sum-exp function for matrices (row-wise)", "topics": [ "log_sum_exp_matrix" ] }, { "page": "log1m", "title": "Log of one minus x", "topics": [ "log1m" ] }, { "page": "log1m_exp", "title": "Log of one minus exponential of x", "topics": [ "log1m_exp" ] }, { "page": "log1p_exp", "title": "Log of one plus exponential of x", "topics": [ "log1p_exp" ] }, { "page": "logit", "title": "Logit function", "topics": [ "logit" ] }, { "page": "lsmeans", "title": "Least Squares Means (Marginal Means)", "topics": [ "lsmeans" ] }, { "page": "make_bw_from_ydif", "title": "Make Best and Worst Responses from Best-Worst Pair Indices", "topics": [ "make_bw_from_ydif", "restore_bw_from_ydif" ] }, { "page": "make_glmer_re_terms", "title": "Prepare GLMM Formula Components", "topics": [ "make_glmer_re_terms" ] }, { "page": "make_glmer_Z_matrix", "title": "Reconstruct an Observation-Level Random-Effect Design Matrix", "topics": [ "make_glmer_Z_matrix" ] }, { "page": "make_init_mdu", "title": "Create Initial Values for Multidimensional Unfolding", "topics": [ "make_init_mdu" ] }, { "page": "make_ydif_from_bw", "title": "Make Best-Worst Pair Indices from Best and Worst Responses", "topics": [ "make_ydif_from_bw" ] }, { "page": "map_est", "title": "Maximum A Posteriori (MAP) Estimate", "topics": [ "map_est" ] }, { "page": "MAP_Fit", "title": "MAP fit object", "topics": [ "MAP_Fit" ] }, { "page": "math_functions", "title": "Mathematical and Matrix Utility Functions for RTMB Models", "concept": [ "utilities" ], "topics": [ "math_functions" ] }, { "page": "MCMC_Fit", "title": "MCMC fit object", "topics": [ "MCMC_Fit" ] }, { "page": "parameter_types", "title": "Parameter Types and Constraints in RTMB Models", "topics": [ "parameter_types" ] }, { "page": "parameters_code", "title": "Code block for parameter definitions", "topics": [ "parameters_code" ] }, { "page": "plot_acf", "title": "Plot autocorrelation for one variable across chains", "topics": [ "plot_acf" ] }, { "page": "plot_conditional_effects", "title": "Plot conditional effects", "topics": [ "plot_conditional_effects" ] }, { "page": "plot_dens", "title": "Plot 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)", "topics": [ "plot.ce_rtmb" ] }, { "page": "plot.rtmb_lsmeans", "title": "Plot marginal means with error bars", "topics": [ "plot.rtmb_lsmeans" ] }, { "page": "print.bayes_factor", "title": "Print method for bayes_factor objects", "topics": [ "print.bayes_factor" ] }, { "page": "print.bayes_factor_rtmb", "title": "Print method for bayes_factor_rtmb objects", "topics": [ "print.bayes_factor_rtmb" ] }, { "page": "print.ce_rtmb", "title": "Print method for ce_rtmb class (automatically calls plot)", "topics": [ "print.ce_rtmb" ] }, { "page": "print.ce_simple", "title": "Print simple effects", "topics": [ "print.ce_simple" ] }, { "page": "print.summary_BayesRTMB", "title": "print for summary_BayesRTMB class", "topics": [ "print.summary_BayesRTMB" ] }, { "page": "prior_flat", "title": "Specify a flat prior", "topics": [ "prior_flat" ] }, { "page": "prior_jzs", "title": "Specify a JZS (Jeffrey-Zellner-Siow) prior for t-tests", "topics": [ "prior_jzs" ] }, { "page": "prior_normal", "title": "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": "rhat_summary", "title": "Summarize MCMC R-hat Values", "topics": [ "rhat_summary", "rhat_summary.default", "rhat_summary.mcmc_fit" ] }, { "page": "rtmb_array", "title": "Create an AD-compatible array", "topics": [ "rtmb_array" ] }, { "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" ] }, { "page": "rtmb_loglinear", "title": "RTMB-based Log-linear analysis (Poisson regression)", "topics": [ "rtmb_loglinear" ] }, { "page": "rtmb_lrt", "title": "Fit a Latent Rank Theory (LRT) Model", "topics": [ "rtmb_lrt" ] }, { "page": "rtmb_mdu", "title": "RTMB-based Multidimensional Unfolding Wrapper", "topics": [ "rtmb_mdu" ] }, { "page": "rtmb_mediation", "title": "RTMB-based Mediation Analysis Wrapper", "topics": [ "rtmb_mediation" ] }, { "page": "rtmb_mixture", "title": "Mixture Model Wrapper for RTMB", "topics": [ "rtmb_mixture" ] }, { "page": "rtmb_model", "title": "Create an RTMB_Model Object", "topics": [ "rtmb_model" ] }, { "page": "RTMB_Model-class", "title": "RTMB model object", "topics": [ "RTMB_Model", "RTMB_Model-class" ] }, { "page": "rtmb_syntax", "title": "Guidelines for Writing RTMB-Compatible Code", "topics": [ "rtmb_syntax" ] }, { "page": "rtmb_table", "title": "RTMB-based Contingency Table Analysis (Chi-squared Test)", "topics": [ "rtmb_table" ] }, { "page": "rtmb_ttest", "title": "RTMB-based Bayesian two-sample t-test wrapper function", "topics": [ "rtmb_ttest" ] }, { "page": "rtmb_vector", "title": "Create an AD-compatible vector", "topics": [ "rtmb_vector" ] }, { "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": "transform_code", "title": "Transformed Code Wrapper for RTMB", "topics": [ "transform_code" ] }, { "page": "upgrade_fit", "title": "Upgrade a saved fit object to the current BayesRTMB class definitions", "topics": [ "upgrade_fit" ] }, { "page": "validate_data", "title": "Pre-validation of data and parameters", "topics": [ "validate_data" ] }, { "page": "VB_Fit", "title": "VB fit object", "topics": [ "VB_Fit" ] } ], "_readme": "https://github.com/norimune/bayesrtmb/raw/HEAD/README.md", "_rundeps": [ "lattice", "MASS", "Matrix", "R6", "Rcpp", "RcppEigen", "RTMB", "TMB" ], "_vignettes": [ { "source": "analysis_reference.Rmd", "filename": "analysis_reference.html", "title": "BayesRTMB Analysis Reference", "engine": "knitr::rmarkdown", "headings": [ "0. How To Read This Page", "Goal-Oriented Quick Navigation", "1. Analysis Workflow", "1.1 Choosing An Estimation Method", "2. Function Overview", "2.1 Model Definition", "2.2 Wrapper Functions", "2.3 Estimation Methods", "3. Common Fit-Object Methods", "3.1 estimate(), EAP(), and MAP()", "3.2 pars And component", "4. MCMC_Fit", "4.1 Main Methods", "4.2 draws()", "4.3 summary() And diagnose()", "4.4 transformed_draws() And generated_quantities()", "4.5 Bridge Sampling And Bayes Factors", "4.6 WAIC", "5. MAP_Fit", "5.1 Main Methods", "5.2 num_estimate And The Best Run", "5.3 Using Optimization Results As Initial Or Fixed Values", "5.4 profile()", "5.5 WAIC For MAP Fits", "6. VB_Fit", "6.1 Main Methods", "6.2 num_estimate And Best Estimate", "6.3 Checking Convergence With plot_elbo()", "6.4 WAIC For VB Fits", "7. Classic_Fit", "7.1 Main Methods", "7.2 ANOVA", "7.3 AIC, BIC, And logLik", "7.4 robust_se()", "7.5 lsmeans()", "8. 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JZS prior で Bayes factor を計算する", "次に読むページ" ], "created": "2026-04-23 11:36:04", "modified": "2026-07-24 05:24:13", "commits": 47 }, { "source": "ja-rtmb_internals.Rmd", "filename": "ja-rtmb_internals.html", "title": "BayesRTMB の内部構造", "engine": "knitr::rmarkdown", "headings": [ "1. 全体の流れ", "2. rtmb_code の各ブロック", "data", "setup", "parameters", "transform", "model", "generate", "3. パラメータ表現", "4. ヤコビアン補正", "5. RTMB の自動微分オブジェクト", "6. 推定メソッドごとの違い", "sample", "optimize", "variational", "classic", "7. optimize と classic の推定結果の違い", "8. Laplace 近似と random", "9. 標準誤差と区間推定", "10. AD 型のコードを書くときの注意点", "パラメータに依存する if 文", "apply 系の関数", "setup と transform を使い分ける", "11. 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Related Articles" ], "created": "2026-05-18 02:13:05", "modified": "2026-07-24 05:24:13", "commits": 5 }, { "source": "introduction.Rmd", "filename": "introduction.html", "title": "Introduction to BayesRTMB", "engine": "knitr::rmarkdown", "headings": [ "What is BayesRTMB?", "Links to Articles", "What BayesRTMB Can Do", "Two Entry Points", "Starting from Wrapper Functions", "Writing Your Own Model", "Choosing an Inference Method", "MCMC", "MAP Estimation", "Variational Inference", "Frequentist Analysis", "Random Effects and Laplace Approximation", "Model Comparison (Bridge Sampling / WAIC)", "Marginal Likelihood and Bayes Factor by Bridge Sampling", "Model Comparison by WAIC", "Next Steps" ], "created": "2026-04-04 09:29:40", "modified": "2026-07-24 05:24:13", "commits": 17 }, { "source": "rtmb_internals.Rmd", "filename": "rtmb_internals.html", "title": "RTMB Internals and Inference Algorithms", "engine": "knitr::rmarkdown", "headings": [ "1. Overall Flow", "2. 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