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  "Package": "guess",
  "Title": "Adjust Estimates of Learning for Guessing",
  "Version": "0.5.0",
  "Authors@R": "c(\nperson(\"Gaurav\", \"Sood\", email = \"gsood07@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Ken\", \"Cor\", email = \"mcor@ualberta.ca\", role = c(\"aut\"))\n)",
  "Description": "Provides tools to adjust estimates of learning for\nguessing-related bias in educational and survey research.\nImplements standard guessing correction methods and a\nsophisticated latent class model that leverages informative\npre-post test transitions to account for guessing behavior. The\npackage helps researchers obtain more accurate estimates of\nactual learning when respondents may guess on closed-ended\nknowledge items. For theoretical background and empirical\nvalidation, see Cor and Sood (2018)\n<https://gsood.com/research/papers/guess.pdf>.",
  "URL": "https://github.com/finite-sample/guess,\nhttps://finite-sample.github.io/guess/",
  "BugReports": "https://github.com/finite-sample/guess/issues",
  "License": "MIT + file LICENSE",
  "VignetteBuilder": "knitr",
  "RoxygenNote": "7.3.3",
  "Encoding": "UTF-8",
  "Language": "en-US",
  "Config/testthat/edition": "3",
  "Repository": "https://finite-sample.r-universe.dev",
  "Date/Publication": "2026-04-08 18:06:25 UTC",
  "RemoteUrl": "https://github.com/finite-sample/guess",
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    "User": "root"
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  "Author": "Gaurav Sood [aut, cre],\nKen Cor [aut]",
  "Maintainer": "Gaurav Sood <gsood07@gmail.com>",
  "MD5sum": "1af43cf27b17ded6082fe963f9b6e863",
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    "name": "finite-sample",
    "description": "econometrics adjacent"
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    "extra/citation.json",
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  "_realowner": "finite-sample",
  "_cranurl": true,
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      "date": "2016-02-08"
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      "date": "2025-12-15"
    },
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      "date": "2026-03-31"
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    "cross_sectional_irt",
    "cross_sectional_learning",
    "cv_individuals",
    "cv_items",
    "estimate_ability",
    "fit_dk",
    "fit_model",
    "fit_nodk",
    "group_adj",
    "lca_adj",
    "lca_cor",
    "lca_fit",
    "lca_irt",
    "lca_se",
    "log_likelihood",
    "multi_transmat",
    "nona",
    "perplexity_individuals",
    "perplexity_items",
    "posterior_class_probs",
    "posterior_learned",
    "simulate_lca",
    "simulate_lca_dk",
    "stnd_cor",
    "transmat",
    "validate_recovery"
  ],
  "_help": [
    {
      "page": "calculate_expected_values",
      "title": "Calculate expected values for goodness of fit test",
      "topics": [
        "calculate_expected_values"
      ]
    },
    {
      "page": "coef.guess_fit",
      "title": "Extract coefficients from guess_fit",
      "topics": [
        "coef.guess_fit"
      ]
    },
    {
      "page": "cross_sectional_irt",
      "title": "Cross-sectional IRT learning probability",
      "topics": [
        "cross_sectional_irt"
      ]
    },
    {
      "page": "cross_sectional_learning",
      "title": "Cross-sectional learning estimate",
      "topics": [
        "cross_sectional_learning"
      ]
    },
    {
      "page": "cv_individuals",
      "title": "K-fold cross-validation over individuals",
      "topics": [
        "cv_individuals"
      ]
    },
    {
      "page": "cv_items",
      "title": "K-fold cross-validation over items",
      "topics": [
        "cv_items"
      ]
    },
    {
      "page": "estimate_ability",
      "title": "Estimate ability from single timepoint (cross-sectional)",
      "topics": [
        "estimate_ability"
      ]
    },
    {
      "page": "fit_model",
      "title": "Goodness of fit statistics for transition matrix data",
      "topics": [
        "fit_dk",
        "fit_model",
        "fit_nodk"
      ]
    },
    {
      "page": "format_transition_matrix",
      "title": "Format transition matrix result with appropriate row and column names",
      "topics": [
        "format_transition_matrix"
      ]
    },
    {
      "page": "group_adj",
      "title": "Group Level Adjustment That Accounts for Propensity to Guess",
      "topics": [
        "group_adj"
      ]
    },
    {
      "page": "lca_adj",
      "title": "Person Level Adjustment",
      "topics": [
        "lca_adj"
      ]
    },
    {
      "page": "lca_cor",
      "title": "Calculate item level and aggregate learning",
      "topics": [
        "lca_cor"
      ]
    },
    {
      "page": "lca_fit",
      "title": "Fit LCA model from individual-level data",
      "topics": [
        "lca_fit"
      ]
    },
    {
      "page": "lca_irt",
      "title": "Estimate LCA model with IRT difficulty parameterization",
      "topics": [
        "lca_irt"
      ]
    },
    {
      "page": "lca_se",
      "title": "Bootstrapped standard errors of effect size estimates",
      "topics": [
        "lca_se"
      ]
    },
    {
      "page": "log_likelihood",
      "title": "Calculate log-likelihood for transition data",
      "topics": [
        "log_likelihood"
      ]
    },
    {
      "page": "multi_transmat",
      "title": "Creates a transition matrix for each item.",
      "topics": [
        "multi_transmat"
      ]
    },
    {
      "page": "nona",
      "title": "No NAs",
      "topics": [
        "nona"
      ]
    },
    {
      "page": "perplexity_individuals",
      "title": "Calculate perplexity from individual-level data",
      "topics": [
        "perplexity_individuals"
      ]
    },
    {
      "page": "perplexity_items",
      "title": "Calculate perplexity from aggregated item data",
      "topics": [
        "perplexity_items"
      ]
    },
    {
      "page": "posterior_class_probs",
      "title": "Compute posterior class probabilities",
      "topics": [
        "posterior_class_probs"
      ]
    },
    {
      "page": "posterior_learned",
      "title": "Compute posterior probability of learning",
      "topics": [
        "posterior_learned"
      ]
    },
    {
      "page": "print.guess_cv",
      "title": "Print method for guess_cv",
      "topics": [
        "print.guess_cv"
      ]
    },
    {
      "page": "print.guess_fit",
      "title": "Print method for guess_fit",
      "topics": [
        "print.guess_fit"
      ]
    },
    {
      "page": "simulate_lca",
      "title": "Simulation Functions for LCA Models",
      "topics": [
        "simulate_lca"
      ]
    },
    {
      "page": "simulate_lca_dk",
      "title": "Simulate Pre-Post Test Data (DK Model)",
      "topics": [
        "simulate_lca_dk"
      ]
    },
    {
      "page": "stnd_cor",
      "title": "Standard Guessing Correction for Learning",
      "topics": [
        "stnd_cor"
      ]
    },
    {
      "page": "summary.guess_cv",
      "title": "Summary method for guess_cv",
      "topics": [
        "summary.guess_cv"
      ]
    },
    {
      "page": "summary.guess_fit",
      "title": "Summary method for guess_fit",
      "topics": [
        "summary.guess_fit"
      ]
    },
    {
      "page": "transmat",
      "title": "transmat: Cross-wave transition matrix",
      "topics": [
        "transmat"
      ]
    },
    {
      "page": "validate_compatible_dataframes",
      "title": "Validate that two data frames have compatible dimensions",
      "topics": [
        "validate_compatible_dataframes"
      ]
    },
    {
      "page": "validate_gamma",
      "title": "Validate gamma parameter",
      "topics": [
        "validate_gamma"
      ]
    },
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      "title": "Validate lucky vector for standard correction",
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        "validate_lucky_vector"
      ]
    },
    {
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      "title": "Validate prior parameters",
      "topics": [
        "validate_priors"
      ]
    },
    {
      "page": "validate_recovery",
      "title": "Validate Parameter Recovery via Monte Carlo Simulation",
      "topics": [
        "validate_recovery"
      ]
    },
    {
      "page": "validate_transition_values",
      "title": "Validate transition matrix values",
      "topics": [
        "validate_transition_values"
      ]
    }
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  "_readme": "https://github.com/finite-sample/guess/raw/HEAD/README.md",
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      "headings": [
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        "Typical Workflow",
        "Step 1: Prepare Your Data",
        "Step 2: Compute Naive Learning Estimate",
        "Step 3: Fit the LCA Model",
        "Step 4: Extract and Interpret Results",
        "Step 5: Get Standard Errors via Bootstrap",
        "Step 6: Assess Model Fit",
        "Step 7: Compare Across Groups (Optional)",
        "The Latent Class Model",
        "Three Latent Classes",
        "Cell Probability Derivation",
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        "Computing Posterior Class Probabilities",
        "Comparison with Cross-Sectional IRT",
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        "Effect of Gamma (Guessing Rate)",
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        "Session Info"
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      "author": "Gaurav Sood",
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      "headings": [
        "guess: Adjust Estimates of Learning for Guessing",
        "Measuring Learning:",
        "Estimand",
        "Other Issues",
        "Standard Correction for Guessing",
        "Latent Class Correction for Guessing",
        "Installation",
        "Usage",
        "Transition Matrix",
        "Adjusting Using the Latent Class Model",
        "Adjust by Groups",
        "Standard Errors",
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        "Cross-validation over items",
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        "Simulation with Don't Know responses",
        "Comprehensive parameter recovery validation",
        "Statistical Efficiency: Sample Size and Parameter Recovery",
        "Sample Size Effect",
        "Number of Items Effect",
        "Parameter Scenarios",
        "Why Individual-Level Data Provides Efficiency Gains"
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      "created": "2015-10-21 01:26:41",
      "modified": "2026-04-08 18:06:25",
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