Package index • BayesRTMB

Core Modeling

Functions for defining and fitting custom models.

rtmb_code(): Define an RTMB Model with Stan-like Syntax
rtmb_model(): Create an RTMB_Model Object

Wrapper Functions

Functions for quick, standard analyses (Classical and Bayesian).

rtmb_lm(): RTMB-based Linear Regression wrapper function
rtmb_glm(): RTMB-based GLM wrapper function (no random effects)
rtmb_lmer(): RTMB-based Linear Mixed Model (LMM) wrapper function
rtmb_glmer(): RTMB-based GLMM wrapper function
rtmb_ttest(): RTMB-based Bayesian two-sample t-test wrapper function
rtmb_corr(): Fit a Correlation Model using RTMB
rtmb_mediation(): RTMB-based Mediation Analysis Wrapper
rtmb_mixture(): Mixture Model Wrapper for RTMB
rtmb_table(): RTMB-based Contingency Table Analysis (Chi-squared Test)
rtmb_loglinear(): RTMB-based Log-linear analysis (Poisson regression)
rtmb_fa(): RTMB-based Factor Analysis Wrapper
rtmb_irt(): RTMB-based IRT (Item Response Theory) Wrapper
rtmb_mdu(): RTMB-based Multidimensional Unfolding Wrapper
rtmb_lrt(): Fit a Latent Rank Theory (LRT) Model

Plotting and Diagnostics

Functions for visualizing MCMC samples and model results.

plot_acf(): Plot autocorrelation for one variable across chains
plot_conditional_effects(): Plot conditional effects
plot_dens(): Plot posterior densities for MCMC samples
plot_forest(): Plot parameter estimates and credible intervals (Forest Plot)
plot_item_curve(): Plot item/category response curves
plot_item_info(): Plot item information functions
plot_lsmeans(): Plot least-squares marginal means
plot_mdu(): Plot Multidimensional Unfolding Configuration
plot_pairs(): Plot pairs for posterior samples
plot_test_info(): Plot test information function
plot_trace(): Plot MCMC trace plots

Post-Estimation and Utilities

Tools for model evaluation and post-processing.

bayes_factor(): Calculate Bayes Factor
conditional_effects(): Calculate Conditional Effects
simple_effects(): Calculate Simple Effects
lsmeans(): Least Squares Means (Marginal Means)
item_curve(): Calculate Item Response Curve / Category Response Curve
item_info(): Calculate Item Information Function
test_info(): Calculate Test Information Function
sort_loadings(): Sort and display factor loadings neatly
read_mcmc_csv(): Restore MCMC Fit from CSV

Math and Transformation Functions

Functions used within rtmb_code for stability and transformations.

logit(): Logit function
inv_logit(): Inverse logit function
log1m(): Log of one minus x
log1m_exp(): Log of one minus exponential of x
log1p_exp(): Log of one plus exponential of x
log_sum_exp(): Log-sum-exp function
log_softmax(): Log-softmax function
softmax(): Softmax function
log_mix(): Log mixture of two probabilities
log_det_chol(): Log determinant of a Cholesky factor
quad_form_chol(): Quadratic form using a Cholesky factor
quad_form_diag(): Quadratic form with a diagonal matrix
distance(): Euclidean distance
squared_distance(): Squared Euclidean distance
fabs(): Smooth absolute value function
to_centered_matrix(): Vector to centered matrix (RTMB compatible)
to_centered_tri(): Vector to centered triangular matrix (RTMB compatible)
sum_to_zero(): Sum-to-zero transformation
stz_basis(): stz basis function

Classes

RTMB_Fit_Base: Base class for RTMB Fit objects
MAP_Fit: MAP fit object
MCMC_Fit: MCMC fit object
VB_Fit: VB fit object
Classic_Fit: Classic fit object

Datasets

beverage: Beverage Preference Data
BigFive: Big Five Personality Traits Data
debate: Debate Simulation Data

Documentation Topics

distributions: Probability Distributions for RTMB Models
math_functions: Mathematical and Matrix Utility Functions for RTMB Models
model_code(): Model Code Wrapper for RTMB
parameters_code(): Code block for parameter definitions
parameter_types: Parameter Types and Constraints in RTMB Models

Other Objects and Internals

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