Package: BayesRTMB 0.1.1

BayesRTMB: Bayesian Inference Using 'RTMB'

Provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the 'RTMB' package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) <https://jmlr.org/papers/v15/hoffman14a.html>.

Authors:Hiroshi Shimizu [aut, cre]

BayesRTMB_0.1.1.tar.gz
BayesRTMB_0.1.1.zip(r-4.7)BayesRTMB_0.1.1.zip(r-4.6)BayesRTMB_0.1.1.zip(r-4.5)
BayesRTMB_0.1.1.tgz(r-4.6-any)BayesRTMB_0.1.1.tgz(r-4.5-any)
BayesRTMB_0.1.1.tar.gz(r-4.7-any)BayesRTMB_0.1.1.tar.gz(r-4.6-any)
BayesRTMB_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BayesRTMB/json (API)
NEWS

# Install 'BayesRTMB' in R:

install.packages('BayesRTMB', repos = c('https://norimune.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/norimune/bayesrtmb/issues

Pkgdown/docs site:https://norimune.github.io

Datasets:

beverage - Beverage Preference Data
BigFive - Big Five Personality Traits Data
debate - Debate Simulation Data
training - Social Skills Training Data

On CRAN:

5.73 score 1 stars 86 exports 8 dependencies

Last updated from:1baeafa3b4. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	185
source / vignettes	OK	228
linux-release-x86_64	OK	186
macos-release-arm64	OK	175
macos-oldrel-arm64	OK	203
windows-devel	OK	159
windows-release	OK	190
windows-oldrel	OK	167
wasm-release	OK	109

Exports:bayes_factor Classic_Fit conditional_effects Dim distance ess_basic ess_bulk ess_tail95 fabs gaussian_process_lpdf inv_logit item_curve item_info log_det_chol log_mix log_softmax log_sum_exp log_sum_exp_matrix log1m log1m_exp log1p_exp logit lsmeans make_bw_from_ydif make_glmer_re_terms make_glmer_Z_matrix make_init_mdu make_ydif_from_bw map_est MAP_Fit MCMC_Fit plot_acf plot_conditional_effects plot_dens plot_forest plot_item_curve plot_item_info plot_lsmeans plot_mdu plot_pairs plot_test_info plot_trace prior_flat prior_jzs prior_normal prior_rhs prior_ssp prior_uniform prior_weak quad_form_chol quad_form_diag quantile95 r_hat read_mcmc_csv restore_bw_from_ydif rtmb_code rtmb_corr rtmb_fa RTMB_Fit_Base rtmb_glm rtmb_glmer rtmb_irt rtmb_lm rtmb_lmer rtmb_loglinear rtmb_lrt rtmb_mdu rtmb_mediation rtmb_mixture rtmb_model RTMB_Model rtmb_table rtmb_ttest safe_rtmb_model simple_effects softmax sort_loadings squared_distance stz_basis sum_to_zero test_info to_centered_matrix to_centered_tri to_long to_wide VB_Fit

Dependencies:lattice MASS Matrix R6 Rcpp RcppEigen RTMB TMB

BayesRTMB Quick Start

Rendered fromquick_start.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-22
Started: 2026-04-27

BayesRTMB の内部構造

Rendered fromja-rtmb_internals.Rmdusingknitr::rmarkdownon Jun 02 2026.

Last update: 2026-05-19
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BayesRTMB の概要

Rendered fromja-introduction.Rmdusingknitr::rmarkdownon Jun 02 2026.

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BayesRTMB クイックスタート

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Hierarchical Models and GLMMs with rtmb_glmer()

Rendered fromrtmb_glmer.Rmdusingknitr::rmarkdownon Jun 02 2026.

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Introduction to BayesRTMB

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RTMB Internals and Inference Algorithms

Rendered fromrtmb_internals.Rmdusingknitr::rmarkdownon Jun 02 2026.

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rtmb_glmer() で階層モデル・GLMM・分散分析を書く

Rendered fromja-rtmb_glmer.Rmdusingknitr::rmarkdownon Jun 02 2026.

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Wrapper Functions

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Writing Model Codes

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モデルの書き方

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ラッパー関数の使い方

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Help page	Topics
Automatic Differentiation Variational Inference (ADVI)	ADVI_method
Calculate Bayes Factor	bayes_factor
Beverage Preference Data	beverage
Big Five Personality Traits Data	BigFive
Classic fit object	Classic_Fit
Calculate Conditional Effects	conditional_effects
Calculate conditional effects for MCMC fit objects	conditional_effects.mcmc_fit
Debate Simulation Data	debate
Define parameter dimensions and types	Dim
Euclidean distance	distance
Probability Distributions for RTMB Models	distributions
Basic Effective Sample Size for a single chain or pooled chains	ess_basic
Calculate Bulk Effective Sample Size	ess_bulk
Calculate Tail Effective Sample Size (at 2.5% and 97.5% quantiles)	ess_tail95
Smooth absolute value function	fabs
Gaussian Process Log-Density (Squared Exponential Kernel)	gaussian_process_lpdf
Generate Random Initial Values	generate_random_init
Inverse logit function	inv_logit
Calculate Item Response Curve / Category Response Curve	item_curve
Item Response Curve for RTMB_Fit_Base	item_curve.RTMB_Fit_Base
Calculate Item Information Function	item_info
Item Information Function for RTMB_Fit_Base	item_info.RTMB_Fit_Base
Log determinant of a Cholesky factor	log_det_chol
Log mixture of two probabilities	log_mix
Log-softmax function	log_softmax
Log-sum-exp function	log_sum_exp
Log-sum-exp function for matrices (row-wise)	log_sum_exp_matrix
Log of one minus x	log1m
Log of one minus exponential of x	log1m_exp
Log of one plus exponential of x	log1p_exp
Logit function	logit
Least Squares Means (Marginal Means)	lsmeans
Make Best and Worst Responses from Best-Worst Pair Indices	make_bw_from_ydif restore_bw_from_ydif
Prepare GLMM Formula Components	make_glmer_re_terms
Reconstruct an Observation-Level Random-Effect Design Matrix	make_glmer_Z_matrix
Create Initial Values for Multidimensional Unfolding	make_init_mdu
Make Best-Worst Pair Indices from Best and Worst Responses	make_ydif_from_bw
Maximum A Posteriori (MAP) Estimate	map_est
MAP fit object	MAP_Fit
Mathematical and Matrix Utility Functions for RTMB Models	math_functions
MCMC fit object	MCMC_Fit
Model Code Wrapper for RTMB	model_code
Parameter Types and Constraints in RTMB Models	parameter_types
Code block for parameter definitions	parameters_code
Plot autocorrelation for one variable across chains	plot_acf
Plot conditional effects	plot_conditional_effects
Plot posterior densities for MCMC samples	plot_dens
Plot parameter estimates and credible intervals (Forest Plot)	plot_forest
Plot item/category response curves	plot_item_curve
Plot item information functions	plot_item_info
Plot least-squares marginal means	plot_lsmeans
Plot Multidimensional Unfolding Configuration	plot_mdu
Plot pairs for posterior samples	plot_pairs
Plot test information function	plot_test_info
Plot MCMC trace plots	plot_trace
Plot method for ce_rtmb class (Base R)	plot.ce_rtmb
Plot marginal means with error bars	plot.rtmb_lsmeans
Print method for bayes_factor objects	print.bayes_factor
Print method for bayes_factor_rtmb objects	print.bayes_factor_rtmb
Print method for ce_rtmb class (automatically calls plot)	print.ce_rtmb
Print simple effects	print.ce_simple
print for summary_BayesRTMB class	print.summary_BayesRTMB
Specify a flat prior	prior_flat
Specify a JZS (Jeffrey-Zellner-Siow) prior for t-tests	prior_jzs
Specify normal/exponential priors for MAP and Bayesian inference	prior_normal
Specify a Regularized Horseshoe prior for continuous shrinkage	prior_rhs
Specify a Spike-and-Slab prior for variable selection	prior_ssp
Specify a flat prior	prior_uniform
Specify a weakly informative prior	prior_weak
Quadratic form using a Cholesky factor	quad_form_chol
Quadratic form with a diagonal matrix	quad_form_diag
Calculate 95% Quantiles	quantile95
Calculate Rank-normalized Split-R-hat	r_hat
Restore MCMC Fit from CSV	read_mcmc_csv
Define an RTMB Model with Stan-like Syntax	rtmb_code
Fit a Correlation Model using RTMB	rtmb_corr
RTMB-based Factor Analysis Wrapper	rtmb_fa
Base class for RTMB Fit objects	RTMB_Fit_Base
RTMB-based GLM wrapper function (no random effects)	rtmb_glm
RTMB-based GLMM wrapper function	rtmb_glmer
RTMB-based IRT (Item Response Theory) Wrapper	rtmb_irt
RTMB-based Linear Regression wrapper function	rtmb_lm
RTMB-based Linear Mixed Model (LMM) wrapper function	rtmb_lmer
RTMB-based Log-linear analysis (Poisson regression)	rtmb_loglinear
Fit a Latent Rank Theory (LRT) Model	rtmb_lrt
RTMB-based Multidimensional Unfolding Wrapper	rtmb_mdu
RTMB-based Mediation Analysis Wrapper	rtmb_mediation
Mixture Model Wrapper for RTMB	rtmb_mixture
Create an RTMB_Model Object	rtmb_model
RTMB model object	RTMB_Model RTMB_Model-class
Guidelines for Writing RTMB-Compatible Code	rtmb_syntax
RTMB-based Contingency Table Analysis (Chi-squared Test)	rtmb_table
RTMB-based Bayesian two-sample t-test wrapper function	rtmb_ttest
Common Features and Arguments of RTMB Wrapper Functions	rtmb_wrappers
Safe RTMB model construction (with error message translation)	safe_rtmb_model
Calculate Simple Effects	simple_effects
Simple effects for MCMC fit objects	simple_effects.mcmc_fit
Softmax function	softmax
Sort and display factor loadings neatly	sort_loadings
Squared Euclidean distance	squared_distance
stz basis function	stz_basis
Sum-to-zero transformation	sum_to_zero
Summary method for ce_rtmb class	summary.ce_rtmb
Calculate Test Information Function	test_info
Vector to centered matrix (RTMB compatible)	to_centered_matrix
Vector to centered triangular matrix (RTMB compatible)	to_centered_tri
Convert Wide Data to Long Format	to_long
Vector to lower triangular matrix (RTMB compatible)	to_lower_tri
Convert Long Data to Wide Format	to_wide
Social Skills Training Data	training
Transformed Code Wrapper for RTMB	transform_code
Pre-validation of data and parameters	validate_data
VB fit object	VB_Fit

Package: BayesRTMB 0.1.1

BayesRTMB: Bayesian Inference Using 'RTMB'

BayesRTMB Quick Start

BayesRTMB の内部構造

BayesRTMB の概要

BayesRTMB クイックスタート

Hierarchical Models and GLMMs with rtmb_glmer()

Introduction to BayesRTMB

RTMB Internals and Inference Algorithms

rtmb_glmer() で階層モデル・GLMM・分散分析を書く

Wrapper Functions

Writing Model Codes

モデルの書き方

ラッパー関数の使い方

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)