Package index • scdbayes

Getting Started

Launch the Shiny app

Identify design, family, hierarchy, and column roles

detect_design_type(): Detect SCD design type
detect_hierarchical_structure(): Detect hierarchical structure in data
detect_hierarchy_levels(): Detect hierarchy levels in SCD data
find_case_var(): Locate the case-level grouping column by common SCED name patterns
find_cluster_var(): Locate a mid-level cluster grouping column
find_nest_var(): Detect a within-case nesting variable by name pattern then structure
find_phase_var(): Locate the phase column by common SCED name patterns
find_study_var(): Locate the study- / institution-level grouping column
find_time_var(): Locate the time column by common SCED name patterns
tier_vars(): Grouping columns that define one data series (tier)
phase_role(): Design role of phase labels
reversal_terms(): Reversal-design piecewise terms
recommend_distribution_family(): Recommend distribution family based on outcome data
validate_scd_data(): Validate SCD data
check_data_size(): Evaluate dataset size against per-task server limits
wwc_design_check(): WWC single-case design standards screening check

Process phases, compute case-level features, generate synthetic data

process_scd_phases(): Build piecewise regression coding for single-case design phases
consolidate_phase_indicators(): Consolidate binary phase indicator columns into a single phase column
standardize_by_case(): Re-express the outcome in within-case residual-SD units
safe_column_names(): Rename data columns to syntactic names
compute_autocorrelation_features(): Lag-1 autocorrelation per phase per case
compute_case_features(): Per-case descriptive features
analyze_covariates(): Analyze continuous and categorical covariates
analyze_design_patterns(): Analyze design patterns
simulate_scd_data(): Simulate SCD data

Bayesian piecewise model formulas, priors, and fits via brms

default_random_effects(): Default random-effects structure of a single-case data set
build_default_random_effects(): Random effects with explicit grouping columns
build_comparison_models(): Build candidate model formulas
has_random_slope(): Detect a random slope in a model formula
treatment_coef(): Identify the treatment coefficient of a piecewise SCD model
add_exposure_offset(): Add an exposure offset to a count-model formula
generate_adaptive_priors(): Generate adaptive priors based on data characteristics
generate_prior_recommendations(): Generate prior recommendations
assemble_brms_priors(): Assemble brms priors from app-level inputs
priors_to_brms(): Convert prior list to brms prior object
is_prior_string(): Check that a prior string is a single distribution call
is_safe_formula(): Check that a model formula uses only data columns and brms term syntax
is_safe_hypothesis(): Check that a hypothesis string uses only model parameters and arithmetic
run_scd_analysis(): Main analysis function
run_model_comparison(): Model comparison via LOO-CV
run_sequential_update(): Sequential Bayesian updating
run_sensitivity_analysis(): Run sensitivity analysis with different priors and outlier handling

Convergence diagnostics and variance-component bootstrap

compute_convergence_diagnostics(): Convergence diagnostics for Bayesian models
bootstrap_variance_components(): Posterior variance components and ICCs
prior_predictive_check(): Prior predictive check for a fitted SCD model

Posterior predictive checks on visual-analysis features and overlay draws

posterior_predictive_features(): Posterior predictive checks on single-case visual-analysis features
ppc_overlay_draws(): Posterior-predictive line draws for a spaghetti overlay
ppc_replicates(): Posterior predictive replicate datasets

Family-native posterior decision summaries, phase contrasts, and marginal contrasts

default_rope(): Default region of practical equivalence for treatment coefficients
summarize_fixed_effect_decisions(): Posterior decision summary for fixed-effect coefficients
summarize_per_case_effects(): Per-case posterior decision summary
summarize_cross_case_consistency(): Posterior probability of consistent intervention direction across cases
summarize_variance_components(): Posterior summary of variance components
compute_phase_contrast(): Posterior phase contrast on the response scale
marginal_contrast(): Population-averaged counterfactual contrast at a common session
summarize_phase_descriptives(): Pre-model descriptive phase summary

Bayesian design-comparable BC-SMD per study and Bayesian random-effects pooling

bc_smd_bayes(): Bayesian design-comparable between-case standardized mean difference
meta_bayes(): Bayesian random-effects meta-analysis of study effect sizes
calculate_meta_summary(): Calculate meta-analytic summary statistics

Simulation-based power for SCD multilevel Bayesian models