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  "Package": "emburden",
  "Title": "Energy Burden Analysis Using Net Energy Return Methodology",
  "Version": "0.6.2",
  "Authors@R": "person(\"Eric\", \"Scheier\", , \"eric@scheier.org\", role = c(\"aut\", \"cre\", \"cph\"))",
  "Description": "Calculate and analyze household energy burden using the\nNet Energy Return aggregation methodology. Functions support\nweighted statistical calculations across geographic and\ndemographic cohorts, with utilities for formatting results into\npublication-ready tables. Methods are based on Scheier &\nKittner (2022) <doi:10.1038/s41467-021-27673-y>.",
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  "BugReports": "https://github.com/ericscheier/emburden/issues",
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  "Repository": "https://ericscheier.r-universe.dev",
  "Date/Publication": "2026-05-29 06:19:47 UTC",
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  "Author": "Eric Scheier [aut, cre, cph]",
  "Maintainer": "Eric Scheier <eric@scheier.org>",
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    "id": "ee870475cccd09fab9b51c35f050536b6b240965",
    "author": "Eric SS <hello@emrgi.com>",
    "committer": "Eric SS <hello@emrgi.com>",
    "message": "fix: zero-pad numeric FIPS to restore leading zero (CT regression)\n\nThe three remaining substr-based geoid-to-state-FIPS calls in\nR/cache_utils.R (x2) and R/lead_data_loaders.R were silently stripping\nthe leading zero from numeric geoids, misclassifying every CT row\n(state FIPS 09 -> \"90\"\\) and any other state-01-09 county passed\nnumerically (AL=01, AK=02, AZ=04, AR=05, CA=06, CO=08, CT=09).\n\nConcrete symptom: substr(as.character(9001), 1, 2) == \"90\" instead of\n\"09\". With CT geoids stored as integers in source CSVs, the\nstate-coverage diagnostic in cache_utils:102/206 silently dropped CT\nfrom the actuals_states tally, and the lead-loader filter at\nlead_data_loaders:222 silently filtered every CT row out of the result.\n\nThis commit:\n\n  1. Adds R/fips_utils.R with .pad_fips() (internal) — infers target\n     width from longest input value (>5 chars = 11-digit tract, >2 chars =\n     5-digit county, else 2-digit state) and zero-pads numeric inputs\n     back to that width.\n\n  2. Adds .extract_state_fips() and .extract_county_fips() (internal)\n     thin wrappers, used by the patched call sites.\n\n  3. Replaces three substr(as.character(x), 1, N) patterns with the\n     padded equivalents at:\n       - R/cache_utils.R:102 (state-coverage diagnostic)\n       - R/cache_utils.R:206 (sample-data check)\n       - R/lead_data_loaders.R:222 (state filter)\n\n  4. Adds tests/testthat/test-fips-leading-zero.R: 26 assertions across\n     5 test groups including the exact \"90\" vs \"09\" symptom for\n     cache_utils, all 7 state-01-09 cases, and NA / empty-vector edge\n     cases. All pass on this commit (devtools::load_all + test_dir).\n\nPrefer internal helpers (dot-prefixed) for now to avoid widening the\npublic surface; if other packages want to consume these, they should\neither depend on emburdengeo which already exports the equivalent\nextract_*_fips + remap_ct_planning_region (patched in parallel on\n2026-05-29), or graduate these to exported in a follow-up.\n\nCo-Authored-By: Claude Opus 4.7 <no",
    "time": 1780035587
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      "table": true,
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      "table": false,
      "tojson": true
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    {
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      "title": "Colorize Text for Knitted Documents",
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      "title": "Compare Energy Burden Between Years",
      "topics": [
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      "title": "Calculate Energy Burden",
      "topics": [
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      "title": "Calculate Energy Return on Investment (EROI)",
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      "title": "Get Dataset Information",
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      "title": "List Available Income Brackets",
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      "source": "jss-emburden.Rmd",
      "filename": "jss-emburden.html",
      "title": "emburden: Temporal Analysis of Household Energy Burden Using Net Energy Return Metrics",
      "author": "Eric Scheier",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Abstract",
        "Introduction",
        "Mathematical foundations",
        "Comparison with energy burden",
        "Energy poverty threshold",
        "The LEAD Tool and temporal analysis",
        "Package design philosophy",
        "Methodology",
        "Data sources",
        "LEAD Tool",
        "REPLICA dataset",
        "Schema normalization across vintages",
        "Data processing",
        "Energy burden indicator calculation",
        "Weighted aggregation",
        "Data quality considerations",
        "Package architecture",
        "Core functions",
        "Data loading functions",
        "Analysis examples",
        "Temporal comparison workflow",
        "Understanding the output",
        "Example 1: State-level temporal analysis",
        "Example 2: Income bracket analysis",
        "Example 3: Multi-state comparison",
        "Example 4: Housing tenure analysis",
        "Example 5: Federal Poverty Line analysis",
        "Example 6: Census tract-level analysis",
        "Discussion",
        "Policy implications",
        "Split-incentive and principal-agent problems",
        "Data limitations and considerations",
        "Iterative proportional fitting constraints",
        "Income measurement challenges",
        "Energy expenditure estimation",
        "Future research directions",
        "Additional vintages",
        "Additional metrics",
        "Spatial analysis enhancements",
        "Causal analysis tools",
        "Comparison with existing tools",
        "Conclusion",
        "References"
      ],
      "created": "2025-11-10 23:37:04",
      "modified": "2025-12-15 14:42:12",
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      "title": "Getting Started with emburden",
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      "headings": [
        "Introduction",
        "Installation",
        "What is Energy Burden?",
        "Quick Example: Single Household",
        "Loading Data",
        "Calculating Metrics from Cohort Data",
        "Aggregating Energy Burden (Critical!)",
        "The WRONG Way",
        "The CORRECT Way: Via Net Energy Return",
        "Analysis by Income Bracket",
        "Identifying High Energy Burden Households",
        "Using calculate_weighted_metrics()",
        "Key Takeaways",
        "Temporal Comparison",
        "Analyzing Energy Burden by Housing Characteristics",
        "Example: Comparing Renters vs Owners by Heating Fuel",
        "Example: Energy Burden by Building Age and Type",
        "Key Insights from Housing Analysis",
        "Next Steps",
        "References"
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        "Mathematical Relationship",
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        "Why This Works: The Mathematics",
        "Computational Advantages (For Aggregation)",
        "Numerical Stability",
        "Error from Incorrect Aggregation",
        "Practical Workflow",
        "Summary",
        "Key Principles",
        "The Correct Formula",
        "Common Mistakes to Avoid",
        "References",
        "Mathematical Appendix",
        "Identity Proofs"
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