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  "Package": "guess",
  "Title": "Adjust Estimates of Learning for Guessing",
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  "Description": "Provides tools to adjust estimates of learning for\nguessing-related bias in educational and survey research.\nImplements standard guessing correction methods and latent\nclass models that leverage informative pre-post test\ntransitions to account for guessing behavior. The package helps\nresearchers obtain more accurate estimates of actual learning\nwhen respondents may guess on closed-ended knowledge items. For\ntheoretical background and empirical validation, see Cor and\nSood (2016) <https://gsood.com/research/papers/guess.pdf>.",
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  "Author": "Gaurav Sood [aut, cre],\nKen Cor [aut]",
  "Maintainer": "Gaurav Sood <gsood07@gmail.com>",
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    "count_item_transitions",
    "count_transitions",
    "cross_sectional_learning",
    "cross_sectional_learning_score",
    "cv_individual_lca",
    "estimate_logit_score",
    "fit_item_lca",
    "fit_item_lca_counts",
    "fit_person_lca",
    "group_adj",
    "lca_adj",
    "lca_se",
    "nona",
    "posterior_class_probs",
    "posterior_learned",
    "score_individual_lca",
    "score_item_lca",
    "simulate_lca",
    "simulate_lca_dk",
    "stnd_cor",
    "validate_recovery"
  ],
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    {
      "page": "assess_item_lca_fit",
      "title": "Assess goodness of fit for an item-level latent-class model",
      "topics": [
        "assess_item_lca_fit"
      ]
    },
    {
      "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": "count_item_transitions",
      "title": "Count transitions for matched test items",
      "topics": [
        "count_item_transitions"
      ]
    },
    {
      "page": "count_transitions",
      "title": "Count cross-wave response transitions",
      "topics": [
        "count_transitions"
      ]
    },
    {
      "page": "cross_sectional_learning",
      "title": "Cross-sectional learning estimate",
      "topics": [
        "cross_sectional_learning"
      ]
    },
    {
      "page": "cross_sectional_learning_score",
      "title": "Cross-Sectional Learning Score",
      "topics": [
        "cross_sectional_learning_score"
      ]
    },
    {
      "page": "cv_individual_lca",
      "title": "Cross-validate an item LCA fit over individuals",
      "topics": [
        "cv_individual_lca"
      ]
    },
    {
      "page": "estimate_logit_score",
      "title": "Estimate a Cross-Sectional Logit Score",
      "topics": [
        "estimate_logit_score"
      ]
    },
    {
      "page": "fit_item_lca",
      "title": "Fit item-wise latent-class models to paired responses",
      "topics": [
        "fit_item_lca"
      ]
    },
    {
      "page": "fit_item_lca_counts",
      "title": "Fit item-wise latent-class models to transition counts",
      "topics": [
        "fit_item_lca_counts"
      ]
    },
    {
      "page": "fit_person_lca",
      "title": "Fit a joint person-level latent-class model",
      "topics": [
        "fit_person_lca"
      ]
    },
    {
      "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_se",
      "title": "Bootstrapped standard errors of effect size estimates",
      "topics": [
        "lca_se"
      ]
    },
    {
      "page": "nona",
      "title": "Normalize NA Responses",
      "topics": [
        "nona"
      ]
    },
    {
      "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": "print.guess_gof",
      "title": "Print method for item-level goodness-of-fit diagnostics",
      "topics": [
        "print.guess_gof"
      ]
    },
    {
      "page": "score_individual_lca",
      "title": "Score an item LCA fit on individual responses",
      "topics": [
        "score_individual_lca"
      ]
    },
    {
      "page": "score_item_lca",
      "title": "Score an item-level latent-class fit against transition counts",
      "topics": [
        "score_item_lca"
      ]
    },
    {
      "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"
      ]
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    {
      "page": "summary.guess_fit",
      "title": "Summary method for guess_fit",
      "topics": [
        "summary.guess_fit"
      ]
    },
    {
      "page": "validate_compatible_dataframes",
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      "topics": [
        "validate_compatible_dataframes"
      ]
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    {
      "page": "validate_gamma",
      "title": "Validate gamma parameter",
      "topics": [
        "validate_gamma"
      ]
    },
    {
      "page": "validate_guessing_probability",
      "title": "Validate guessing probabilities for standard correction",
      "topics": [
        "validate_guessing_probability"
      ]
    },
    {
      "page": "validate_priors",
      "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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        "Step 5: Get Standard Errors via Bootstrap",
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        "Step 7: Compare Across Groups (Optional)",
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        "Session Info"
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      "modified": "2026-09-28 00:44:59",
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      "title": "Using guess",
      "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",
        "Fit",
        "Model Criticism: Perplexity and Cross-Validation",
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        "Cross-validation over individuals",
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        "Basic simulation (No-DK model)",
        "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",
        "When Individual-Level Data Adds Information"
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      "created": "2016-02-09 02:29:05",
      "modified": "2026-09-28 00:44:59",
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