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  "Title": "Measurement and Linear Hypothesis Models for Lambda Star",
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  "Description": "Estimates intrinsic and captured noncentrality from\nparallel measurements and evaluates the numerical parsimony\nfunctional for an explicitly encoded hypothesis matrix,\nformula, or compatible linear model. Includes common-case model\ncomparisons, quantized entropy capacity, controlled temperature\nintegration, and explicit singularity diagnostics. Uses\nmanuscript projection estimates by default, with an explicit\nalternative population estimator. Supports common nuisance\nadjustment and ordinary case or cluster percentile bootstrap\nintervals, with diagnostics for undefined estimates and\npreserved model coding. Provides reusable row-bound design\nspecifications and explicit conditional term blocks, named\nlinear restrictions, explicit predictor-grid contrasts, and\nfixed or reevaluated basis recipes under a homogeneous\nisotropic measurement-fluctuation assumption. Supports explicit\nknown-reference mean hypotheses and paired differences from\nparallel measurement pairs. Encodes fixed person-by-occasion\nmodels with parallel indicators, implicit person adjustment and\nwhole-person bootstrap with distinct sampled copies. The\nunderlying method is described in Hammes (2026)\n<doi:10.5281/zenodo.22962377>.",
  "License": "Apache License (== 2.0)",
  "Copyright": "2026 Mike Hammes",
  "Encoding": "UTF-8",
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  "Maintainer": "Mike Hammes <mike.hammes@mikehammes.name>",
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    "lambda_contrast",
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    "lambda_measure",
    "lambda_model",
    "lambda_paired",
    "lambda_panel",
    "lambda_reference",
    "make_eta_numerical_lookup",
    "project_onto_basis",
    "thermodynamic_model_state",
    "xi_a",
    "xi_eta_app",
    "xi_eta_numerical"
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      "page": "center_columns",
      "title": "Center a numeric matrix",
      "topics": [
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      "title": "Extract Lambda Star percentile intervals",
      "topics": [
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        "confint.lambda_measurement",
        "confint.lambda_model"
      ]
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      "title": "Encoded design volume",
      "topics": [
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      ]
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      "page": "estimate_lambda_partition_items",
      "title": "Estimate the noncentrality partition for an explicit design",
      "topics": [
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      ]
    },
    {
      "page": "estimate_lambda_star_items",
      "title": "Estimate intrinsic structure from parallel measurements",
      "topics": [
        "estimate_lambda_star_items"
      ]
    },
    {
      "page": "eta_app",
      "title": "Smooth capacity approximation and geometric activation",
      "topics": [
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        "geometry_activation",
        "xi_a",
        "xi_eta_app"
      ]
    },
    {
      "page": "eta_numerical",
      "title": "Quantized maximum-entropy capacity",
      "topics": [
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    },
    {
      "page": "hypothesis_basis",
      "title": "Construct a centered hypothesis basis",
      "topics": [
        "hypothesis_basis"
      ]
    },
    {
      "page": "lambda_bootstrap_control",
      "title": "Settings for case or cluster percentile bootstrap intervals",
      "topics": [
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      ]
    },
    {
      "page": "lambda_compare",
      "title": "Compare two models on one common measurement matrix",
      "topics": [
        "lambda_compare"
      ]
    },
    {
      "page": "lambda_contrast",
      "title": "Encode a homogeneous linear coefficient hypothesis",
      "topics": [
        "lambda_contrast"
      ]
    },
    {
      "page": "lambda_control",
      "title": "Numerical settings for the Lambda Star core",
      "topics": [
        "lambda_control"
      ]
    },
    {
      "page": "lambda_design",
      "title": "Bind a reusable encoded model specification to input rows",
      "topics": [
        "lambda_design",
        "print.lambda_design"
      ]
    },
    {
      "page": "lambda_hypothesis",
      "title": "Construct a conditional block from full-model terms",
      "topics": [
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    },
    {
      "page": "lambda_inference",
      "title": "Estimator selection and ordinary percentile bootstrap",
      "topics": [
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      "page": "lambda_marginal_contrast",
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      "topics": [
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    {
      "page": "lambda_measure",
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      "topics": [
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    },
    {
      "page": "lambda_model",
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      "topics": [
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      ]
    },
    {
      "page": "lambda_paired",
      "title": "Evaluate paired mean differences from parallel measurement pairs",
      "topics": [
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      ]
    },
    {
      "page": "lambda_panel",
      "title": "Encode fixed models on person-by-occasion observations",
      "topics": [
        "lambda_panel"
      ]
    },
    {
      "page": "lambda_reference",
      "title": "Encode a hypothesis relative to a known reference",
      "topics": [
        "lambda_reference"
      ]
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    {
      "page": "lambdastar",
      "title": "Lambda Star: measurement, linear hypotheses and percentile intervals",
      "topics": [
        "lambdastar-package",
        "lambdastar"
      ]
    },
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      "title": "Build a reusable capacity integral lookup",
      "topics": [
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      ]
    },
    {
      "page": "lambda_results",
      "title": "Inspect Lambda Star results",
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        "print.lambda_ci",
        "print.lambda_comparison",
        "print.lambda_comparison_summary",
        "print.lambda_measurement",
        "print.lambda_model",
        "print.lambda_summary",
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        "summary.lambda_measurement",
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