| lambdastar-package | Lambda Star: measurement, linear hypotheses and percentile intervals |
| center_columns | Center a numeric matrix |
| confint.lambda_comparison | Extract Lambda Star percentile intervals |
| confint.lambda_measurement | Extract Lambda Star percentile intervals |
| confint.lambda_model | Extract Lambda Star percentile intervals |
| design_volume | Encoded design volume |
| estimate_lambda_partition_items | Estimate the noncentrality partition for an explicit design |
| estimate_lambda_star_items | Estimate intrinsic structure from parallel measurements |
| eta_app | Smooth capacity approximation and geometric activation |
| eta_numerical | Quantized maximum-entropy capacity |
| geometry_activation | Smooth capacity approximation and geometric activation |
| hypothesis_basis | Construct a centered hypothesis basis |
| lambdastar | Lambda Star: measurement, linear hypotheses and percentile intervals |
| lambda_bootstrap_control | Settings for case or cluster percentile bootstrap intervals |
| lambda_compare | Compare two models on one common measurement matrix |
| lambda_contrast | Encode a homogeneous linear coefficient hypothesis |
| lambda_control | Numerical settings for the Lambda Star core |
| lambda_design | Bind a reusable encoded model specification to input rows |
| lambda_hypothesis | Construct a conditional block from full-model terms |
| lambda_inference | Estimator selection and ordinary percentile bootstrap |
| lambda_marginal_contrast | Encode contrasts of explicitly specified predictor settings |
| lambda_measure | Prepare parallel measurements from a common data frame |
| lambda_model | Evaluate a complete linear hypothesis model |
| lambda_paired | Evaluate paired mean differences from parallel measurement pairs |
| lambda_panel | Encode fixed models on person-by-occasion observations |
| lambda_reference | Encode a hypothesis relative to a known reference |
| lambda_results | Inspect Lambda Star results |
| make_eta_numerical_lookup | Build a reusable capacity integral lookup |
| print.lambda_ci | Inspect Lambda Star results |
| print.lambda_comparison | Inspect Lambda Star results |
| print.lambda_comparison_summary | Inspect Lambda Star results |
| print.lambda_design | Bind a reusable encoded model specification to input rows |
| print.lambda_measurement | Inspect Lambda Star results |
| print.lambda_model | Inspect Lambda Star results |
| print.lambda_summary | Inspect Lambda Star results |
| project_onto_basis | Project a vector using an orthonormal basis |
| summary.lambda_comparison | Inspect Lambda Star results |
| summary.lambda_measurement | Inspect Lambda Star results |
| summary.lambda_model | Inspect Lambda Star results |
| thermodynamic_model_state | Evaluate the parsimony functional for one encoded hypothesis |
| xi_a | Smooth capacity approximation and geometric activation |
| xi_eta_app | Smooth capacity approximation and geometric activation |
| xi_eta_numerical | Integrate numerical capacity over temperature |