{
  "_id": "6a1d673a1d7bb097a0a4d302",
  "Package": "catalytic",
  "Title": "Tools for Applying Catalytic Priors in Statistical Modeling",
  "Version": "0.1.0",
  "Authors@R": "c(\nperson(\"Yitong\", \"Wu\", , \"ywu039@e.ntu.edu.sg\", role = \"aut\",\ncomment = c(ORCID = \"0009-0000-8683-5129\")),\nperson(\"Dongming\", \"Huang\", , \"huang.dongming@nus.edu.sg\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4265-7708\")),\nperson(\"Weihao\", \"Li\", , \"weihao.li@u.nus.edu\", role = \"aut\"),\nperson(\"Ministry of Education, Singapore\", role = \"fnd\",\ncomment = \"The development of this package is supported by the Ministry of Education, Singapore, under the Academic Research Fund Tier 1 A-8000466-00-00 (FY2022).\")\n)",
  "Description": "To improve estimation accuracy and stability in\nstatistical modeling, catalytic prior distributions are\nemployed, integrating observed data with synthetic data\ngenerated from a simpler model's predictive distribution. This\napproach enhances model robustness, stability, and flexibility\nin complex data scenarios. The catalytic prior distributions\nare introduced by 'Huang et al.' (2020,\n<doi:10.1073/pnas.1920913117>), Li and Huang (2023,\n<doi:10.48550/arXiv.2312.01411>).",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.1",
  "Config/testthat/edition": "3",
  "LazyData": "true",
  "VignetteBuilder": "knitr",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-01 11:00:20 UTC",
    "User": "root"
  },
  "Author": "Yitong Wu [aut] (<https://orcid.org/0009-0000-8683-5129>),\nDongming Huang [aut, cre]\n(<https://orcid.org/0000-0003-4265-7708>), Weihao Li [aut],\nMinistry of Education, Singapore [fnd] (The development of this\npackage is supported by the Ministry of Education, Singapore,\nunder the Academic Research Fund Tier 1 A-8000466-00-00\n(FY2022).)",
  "Maintainer": "Dongming Huang <huang.dongming@nus.edu.sg>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2024-12-18 16:42:26 UTC",
  "RemoteUrl": "https://github.com/cran/catalytic",
  "RemoteRef": "HEAD",
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  "MD5sum": "4f1d662e21abbe8bba51dd3a92b00215",
  "_user": "cran",
  "_type": "src",
  "_file": "catalytic_0.1.0.tar.gz",
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  "_sha256": "b5e6ea503ddd08253f6c5719dfb20ace3498cd76b8f540c17b9e27cd8e40768d",
  "_created": "2026-06-01T11:00:20.000Z",
  "_published": "2026-06-01T11:04:26.452Z",
  "_distro": "noble",
  "_jobs": [
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    "author": "Dongming Huang <huang.dongming@nus.edu.sg>",
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    "message": "version 0.1.0\n",
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  "_assets": [
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    "extra/contents.json",
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  "_exports": [
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    "cat_cox_bayes",
    "cat_cox_bayes_joint",
    "cat_cox_initialization",
    "cat_cox_tune",
    "cat_glm",
    "cat_glm_bayes",
    "cat_glm_bayes_joint",
    "cat_glm_bayes_joint_gibbs",
    "cat_glm_initialization",
    "cat_glm_tune",
    "cat_lmm",
    "cat_lmm_initialization",
    "cat_lmm_tune",
    "traceplot"
  ],
  "_datasets": [
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      "title": "Simulated SWIM Dataset with Binary Response",
      "object": "swim",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "cat_cox",
      "title": "Catalytic Cox Proportional Hazards Model (COX) Fitting Function with Fixed Tau",
      "topics": [
        "cat_cox"
      ]
    },
    {
      "page": "cat_cox_bayes",
      "title": "Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with Fixed tau",
      "topics": [
        "cat_cox_bayes"
      ]
    },
    {
      "page": "cat_cox_bayes_joint",
      "title": "Bayesian Estimation for Catalytic Cox Proportional-Hazards Model (COX) with adaptive tau",
      "topics": [
        "cat_cox_bayes_joint"
      ]
    },
    {
      "page": "cat_cox_initialization",
      "title": "Initialization for Catalytic Cox proportional hazards model (COX)",
      "topics": [
        "cat_cox_initialization"
      ]
    },
    {
      "page": "cat_cox_tune",
      "title": "Catalytic Cox Proportional-Hazards Model (COX) Fitting Function by Tuning tau from a Sequence of tau Values",
      "topics": [
        "cat_cox_tune"
      ]
    },
    {
      "page": "cat_glm",
      "title": "Catalytic Generalized Linear Models (GLMs) Fitting Function with Fixed Tau",
      "topics": [
        "cat_glm"
      ]
    },
    {
      "page": "cat_glm_bayes",
      "title": "Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with Fixed tau",
      "topics": [
        "cat_glm_bayes"
      ]
    },
    {
      "page": "cat_glm_bayes_joint",
      "title": "Bayesian Estimation for Catalytic Generalized Linear Models (GLMs) with adaptive tau",
      "topics": [
        "cat_glm_bayes_joint"
      ]
    },
    {
      "page": "cat_glm_bayes_joint_gibbs",
      "title": "Bayesian Estimation with Gibbs Sampling for Catalytic Generalized Linear Models (GLMs) Binomial Family for Coefficients and tau",
      "topics": [
        "cat_glm_bayes_joint_gibbs"
      ]
    },
    {
      "page": "cat_glm_initialization",
      "title": "Initialization for Catalytic Generalized Linear Models (GLMs)",
      "topics": [
        "cat_glm_initialization"
      ]
    },
    {
      "page": "cat_glm_tune",
      "title": "Catalytic Generalized Linear Models (GLMs) Fitting Function by Tuning tau from a Sequence of tau Values",
      "topics": [
        "cat_glm_tune"
      ]
    },
    {
      "page": "cat_lmm",
      "title": "Catalytic Linear Mixed Model (LMM) Fitting Function with fixed tau",
      "topics": [
        "cat_lmm"
      ]
    },
    {
      "page": "cat_lmm_initialization",
      "title": "Initialization for Catalytic Linear Mixed Model (LMM)",
      "topics": [
        "cat_lmm_initialization"
      ]
    },
    {
      "page": "cat_lmm_tune",
      "title": "Catalytic Linear Mixed Model (LMM) Fitting Function by Tuning tau from a Sequence of tau Values",
      "topics": [
        "cat_lmm_tune"
      ]
    },
    {
      "page": "cross_validation",
      "title": "Perform Cross-Validation for Model Estimation",
      "topics": [
        "cross_validation"
      ]
    },
    {
      "page": "cross_validation_cox",
      "title": "Perform Cross-Validation for Catalytic Cox Proportional-Hazards Model (COX) to Select Optimal tau",
      "topics": [
        "cross_validation_cox"
      ]
    },
    {
      "page": "cross_validation_lmm",
      "title": "Perform Cross-Validation for Catalytic Linear Mixed Model (LMM) to Select Optimal tau",
      "topics": [
        "cross_validation_lmm"
      ]
    },
    {
      "page": "extract_coefs",
      "title": "Extract and Format Model Coefficients",
      "topics": [
        "extract_coefs"
      ]
    },
    {
      "page": "extract_dim",
      "title": "Extract Dimension Information from Model Initialization",
      "topics": [
        "extract_dim"
      ]
    },
    {
      "page": "extract_stan_summary",
      "title": "Extract and Format Summary of Stan Model Results",
      "topics": [
        "extract_stan_summary"
      ]
    },
    {
      "page": "extract_tau_seq",
      "title": "Extract and Format Sequence of Tau Values",
      "topics": [
        "extract_tau_seq"
      ]
    },
    {
      "page": "get_adjusted_cat_init",
      "title": "Adjusted Cat Initialization",
      "topics": [
        "get_adjusted_cat_init"
      ]
    },
    {
      "page": "get_cox_gradient",
      "title": "Compute the Gradient for Cox Proportional Hazards Model",
      "topics": [
        "get_cox_gradient"
      ]
    },
    {
      "page": "get_cox_hessian",
      "title": "Compute the Hessian Matrix for Cox Proportional Hazards Model",
      "topics": [
        "get_cox_hessian"
      ]
    },
    {
      "page": "get_cox_kappa",
      "title": "Estimate the kappa value for the synthetic Cox proportional hazards model",
      "topics": [
        "get_cox_kappa"
      ]
    },
    {
      "page": "get_cox_partial_likelihood",
      "title": "Compute the Partial Likelihood for the Cox Proportional Hazards Model",
      "topics": [
        "get_cox_partial_likelihood"
      ]
    },
    {
      "page": "get_cox_qr_solve",
      "title": "Solve Linear System using QR Decomposition",
      "topics": [
        "get_cox_qr_solve"
      ]
    },
    {
      "page": "get_cox_risk_and_failure_sets",
      "title": "Calculate Risk and Failure Sets for Cox Proportional Hazards Model",
      "topics": [
        "get_cox_risk_and_failure_sets"
      ]
    },
    {
      "page": "get_cox_risk_set_idx",
      "title": "Identify the risk set indices for Cox proportional hazards model",
      "topics": [
        "get_cox_risk_set_idx"
      ]
    },
    {
      "page": "get_cox_syn_gradient",
      "title": "Compute the gradient of the synthetic Cox proportional hazards model",
      "topics": [
        "get_cox_syn_gradient"
      ]
    },
    {
      "page": "get_cox_syn_hessian",
      "title": "Compute the Synthetic Hessian Matrix for Cox Proportional Hazards Model",
      "topics": [
        "get_cox_syn_hessian"
      ]
    },
    {
      "page": "get_discrepancy",
      "title": "Compute Discrepancy Measures",
      "topics": [
        "get_discrepancy"
      ]
    },
    {
      "page": "get_formula_lhs",
      "title": "Extract Left-Hand Side of Formula as String",
      "topics": [
        "get_formula_lhs"
      ]
    },
    {
      "page": "get_formula_rhs",
      "title": "Extract the Right-Hand Side of a Formula",
      "topics": [
        "get_formula_rhs"
      ]
    },
    {
      "page": "get_formula_string",
      "title": "Convert Formula to String",
      "topics": [
        "get_formula_string"
      ]
    },
    {
      "page": "get_glm_custom_var",
      "title": "Get Custom Variance for Generalized Linear Model (GLM)",
      "topics": [
        "get_glm_custom_var"
      ]
    },
    {
      "page": "get_glm_diag_approx_cov",
      "title": "Compute Diagonal Approximate Covariance Matrix",
      "topics": [
        "get_glm_diag_approx_cov"
      ]
    },
    {
      "page": "get_glm_family_string",
      "title": "Retrieve GLM Family Name or Name with Link Function",
      "topics": [
        "get_glm_family_string"
      ]
    },
    {
      "page": "get_glm_lambda",
      "title": "Compute Lambda Based on Discrepancy Method",
      "topics": [
        "get_glm_lambda"
      ]
    },
    {
      "page": "get_glm_log_density",
      "title": "Compute Log Density Based on GLM Family",
      "topics": [
        "get_glm_log_density"
      ]
    },
    {
      "page": "get_glm_log_density_grad",
      "title": "Compute Gradient of Log Density for GLM Families",
      "topics": [
        "get_glm_log_density_grad"
      ]
    },
    {
      "page": "get_glm_mean",
      "title": "Compute Mean Based on GLM Family",
      "topics": [
        "get_glm_mean"
      ]
    },
    {
      "page": "get_glm_sample_data",
      "title": "Generate Sample Data for GLM",
      "topics": [
        "get_glm_sample_data"
      ]
    },
    {
      "page": "get_hmc_mcmc_result",
      "title": "Run Hamiltonian Monte Carlo to Get MCMC Sample Result",
      "topics": [
        "get_hmc_mcmc_result"
      ]
    },
    {
      "page": "get_linear_predictor",
      "title": "Compute Linear Predictor",
      "topics": [
        "get_linear_predictor"
      ]
    },
    {
      "page": "get_resampled_df",
      "title": "Resampling Methods for Data Processing",
      "topics": [
        "get_resampled_df"
      ]
    },
    {
      "page": "get_stan_model",
      "title": "Generate Stan Model Based on Specified Parameters",
      "topics": [
        "get_stan_model"
      ]
    },
    {
      "page": "get_standardized_data",
      "title": "Standardize Data",
      "topics": [
        "get_standardized_data"
      ]
    },
    {
      "page": "hmc_neal_2010",
      "title": "Hamiltonian Monte Carlo (HMC) Implementation",
      "topics": [
        "hmc_neal_2010"
      ]
    },
    {
      "page": "is.continuous",
      "title": "Check if a Variable is Continuous",
      "topics": [
        "is.continuous"
      ]
    },
    {
      "page": "mallowian_estimate",
      "title": "Perform Mallowian Estimate for Model Risk (Only Applicable for Gaussian Family)",
      "topics": [
        "mallowian_estimate"
      ]
    },
    {
      "page": "parametric_bootstrap",
      "title": "Perform Parametric Bootstrap for Model Risk Estimation",
      "topics": [
        "parametric_bootstrap"
      ]
    },
    {
      "page": "plot.cat_tune",
      "title": "Plot Likelihood or Risk Estimate vs. Tau for Tuning Model",
      "topics": [
        "plot.cat_tune"
      ]
    },
    {
      "page": "predict.cat_cox",
      "title": "Predict Linear Predictor for New Data Using a Fitted Cox Model",
      "topics": [
        "predict.cat_cox"
      ]
    },
    {
      "page": "predict.cat_glm",
      "title": "Predict Outcome for New Data Using a Fitted GLM Model",
      "topics": [
        "predict.cat_glm"
      ]
    },
    {
      "page": "predict.cat_lmm",
      "title": "Predict Linear Predictor for New Data Using a Fitted Linear Mixed Model",
      "topics": [
        "predict.cat_lmm"
      ]
    },
    {
      "page": "print_df_head_tail",
      "title": "Print Data Frame with Head and Tail Rows",
      "topics": [
        "print_df_head_tail"
      ]
    },
    {
      "page": "print_glm_bayes_joint_binomial_suggestion",
      "title": "Generate Suggestions for Bayesian Joint Binomial GLM Parameter Estimation",
      "topics": [
        "print_glm_bayes_joint_binomial_suggestion"
      ]
    },
    {
      "page": "print.cat",
      "title": "Print Method for 'cat' Object",
      "topics": [
        "print.cat"
      ]
    },
    {
      "page": "print.cat_bayes",
      "title": "Print Summary of 'cat_bayes' Model",
      "topics": [
        "print.cat_bayes"
      ]
    },
    {
      "page": "print.cat_gibbs",
      "title": "Print Summary of 'cat_gibbs' Model",
      "topics": [
        "print.cat_gibbs"
      ]
    },
    {
      "page": "print.cat_initialization",
      "title": "Print Summary for Catalytic Initialization Model",
      "topics": [
        "print.cat_initialization"
      ]
    },
    {
      "page": "print.cat_tune",
      "title": "Print Method for 'cat_tune' Object",
      "topics": [
        "print.cat_tune"
      ]
    },
    {
      "page": "steinian_estimate",
      "title": "Perform Steinian Estimate for Model Risk (Only Applicable for Binomial Family)",
      "topics": [
        "steinian_estimate"
      ]
    },
    {
      "page": "swim",
      "title": "Simulated SWIM Dataset with Binary Response",
      "topics": [
        "swim"
      ]
    },
    {
      "page": "traceplot",
      "title": "Traceplot for Bayesian Model Sampling",
      "topics": [
        "traceplot"
      ]
    },
    {
      "page": "traceplot.cat_bayes",
      "title": "Traceplot for Bayesian Sampling Model",
      "topics": [
        "traceplot.cat_bayes"
      ]
    },
    {
      "page": "traceplot.cat_gibbs",
      "title": "Traceplot for Gibbs Sampling Model",
      "topics": [
        "traceplot.cat_gibbs"
      ]
    },
    {
      "page": "update_lmm_variance",
      "title": "Calculates the log-likelihood for linear mixed models (LMMs) by combining observed and synthetic log-likelihoods based on the variance parameters.",
      "topics": [
        "update_lmm_variance"
      ]
    },
    {
      "page": "validate_cox_initialization_input",
      "title": "Validate Inputs for Catalytic Cox proportional hazards model (COX) Initialization",
      "topics": [
        "validate_cox_initialization_input"
      ]
    },
    {
      "page": "validate_cox_input",
      "title": "Validate Inputs for Catalytic Cox Model",
      "topics": [
        "validate_cox_input"
      ]
    },
    {
      "page": "validate_glm_initialization_input",
      "title": "Validate Inputs for Catalytic Generalized Linear Models (GLMs) Initialization",
      "topics": [
        "validate_glm_initialization_input"
      ]
    },
    {
      "page": "validate_glm_input",
      "title": "Validate Inputs for Catalytic Generalized Linear Models (GLMs)",
      "topics": [
        "validate_glm_input"
      ]
    },
    {
      "page": "validate_lmm_initialization_input",
      "title": "Validate Inputs for Catalytic Linear Mixed Model (LMM) Initialization",
      "topics": [
        "validate_lmm_initialization_input"
      ]
    },
    {
      "page": "validate_lmm_input",
      "title": "Validate Inputs for Catalytic Linear Mixed Model (LMM)",
      "topics": [
        "validate_lmm_input"
      ]
    },
    {
      "page": "validate_positive",
      "title": "Validate Positive or Non-negative Parameter",
      "topics": [
        "validate_positive"
      ]
    }
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