Package index • svyder

Core Pipeline

der_diagnose(): All-in-One DER Diagnostic
der_compute(): Compute Design Effect Ratios
der_classify(): Classify Parameters by Design Sensitivity
der_correct(): Apply Selective Cholesky Correction

Analysis Tools

der_decompose(): Decompose DER into Components
der_sensitivity(): Sensitivity Analysis Across Threshold Values
der_theorem_check(): Verify Theoretical DER Predictions
der_compare(): Compare DER Across Clustering Definitions

Output Methods

print(<svyder>): Print a svyder Object
summary(<svyder>): Summarize a svyder Object
tidy.svyder(): Tidy a svyder Object
glance.svyder(): Glance at a svyder Object
as.matrix(<svyder>): Extract Draws Matrix from a svyder Object

Visualization

plot(<svyder>): Plot DER Diagnostic Results
autoplot.svyder(): Create a ggplot2 Visualization of DER Results

Backend Integration

extract_draws(): Extract Posterior Draws from Model Objects
extract_design(): Extract Survey Design Information

Classes

new_svyder(): Low-Level Constructor for svyder Objects
validate_svyder(): Validate a svyder Object
is.svyder(): Test if an Object is a svyder Object

Datasets

nsece_demo: Synthetic NSECE-Like Survey Data
sim_hlr: Simulated Hierarchical Linear Regression Data