{
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  "Package": "DSDRM",
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  "Title": "Distributed Sampling for Dynamic Regression Models",
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  "Description": "A toolbox for distributed dynamic regression modeling,\nparallel estimation, multiple distributed sampling algorithms\n(Metropolis-Hastings, block bootstrap, adaptive,\nhypergeometric), sparse matrix optimization, model\nvisualization, prediction and performance evaluation.  The\nphilosophy of the package is described in Guo (2025)\n<doi:10.1038/s41598-025-93333-6>.",
  "License": "Apache License 2.0",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.3",
  "Author": "Guangbao Guo [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-4115-6218>)",
  "Maintainer": "Guangbao Guo <ggb11111111@163.com>",
  "Config/testthat/edition": "3",
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  "Language": "en-US",
  "Packaged": {
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  "Date/Publication": "2026-07-10 19:30:02 UTC",
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    "block_data_split",
    "calc_metrics",
    "dist_parallel_estimate",
    "dist_qn_algorithm",
    "dist_qn_with_time",
    "dsdrm_fit",
    "dsdrm_generate_data",
    "dsdrm_metrics",
    "dsdrm_predict",
    "dsdrm_sampling",
    "dynamic_coef_plot",
    "dynamic_opt_omega",
    "gamma_converge",
    "gamma_fusion",
    "global_info_mat",
    "global_mcmc_estimator",
    "global_mcmc_with_time",
    "global_posterior_gamma",
    "global_quasi_ll",
    "global_score",
    "init_gamma",
    "local_marginal_likelihood",
    "local_quasi_ll",
    "mh_gamma_update",
    "parallel_estimate",
    "penalized_quasi_ll",
    "run_batch_simulation",
    "run_batch_simulation_with_time",
    "sparse_matrix_optim",
    "static_dist_with_time",
    "static_distributed_estimator"
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    {
      "page": "adaptive_block_len",
      "title": "Compute Adaptive Block Length",
      "topics": [
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      ]
    },
    {
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      "title": "Split Time Series Data into Distributed Blocks",
      "topics": [
        "block_data_split"
      ]
    },
    {
      "page": "calc_metrics",
      "title": "Simulation Evaluation Metrics: MSE, MAE, R2, and Model Selection Accuracy",
      "topics": [
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      ]
    },
    {
      "page": "dist_parallel_estimate",
      "title": "Fully Distributed Parallel Estimation",
      "topics": [
        "dist_parallel_estimate"
      ]
    },
    {
      "page": "dist_qn_algorithm",
      "title": "Distributed Quasi-Newton Iteration Main Algorithm",
      "topics": [
        "dist_qn_algorithm"
      ]
    },
    {
      "page": "dist_qn_with_time",
      "title": "DSDRM Algorithm with Computation Time Tracking",
      "topics": [
        "dist_qn_with_time"
      ]
    },
    {
      "page": "dsdrm_fit",
      "title": "DSDRM Model Fitting (Main Interface)",
      "topics": [
        "dsdrm_fit"
      ]
    },
    {
      "page": "dsdrm_generate_data",
      "title": "Generate Time-Varying Distributed Dynamic Regression Data",
      "topics": [
        "dsdrm_generate_data"
      ]
    },
    {
      "page": "dsdrm_metrics",
      "title": "DSDRM Comprehensive Performance Metrics",
      "topics": [
        "dsdrm_metrics"
      ]
    },
    {
      "page": "dsdrm_predict",
      "title": "Out-of-Sample Prediction for DSDRM",
      "topics": [
        "dsdrm_predict"
      ]
    },
    {
      "page": "dsdrm_sampling",
      "title": "DSDRM with Posterior Sampling (MCMC-based)",
      "topics": [
        "dsdrm_sampling"
      ]
    },
    {
      "page": "dynamic_coef_plot",
      "title": "Dynamic Coefficient Path Plot",
      "topics": [
        "dynamic_coef_plot"
      ]
    },
    {
      "page": "dynamic_opt_omega",
      "title": "Compute Dynamic Optimal Sampling Weights",
      "topics": [
        "dynamic_opt_omega"
      ]
    },
    {
      "page": "gamma_converge",
      "title": "Convergence Check for Model Structure",
      "topics": [
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      ]
    },
    {
      "page": "gamma_fusion",
      "title": "Fusion of Block-wise Model Selection Results (Weighted Voting)",
      "topics": [
        "gamma_fusion"
      ]
    },
    {
      "page": "global_info_mat",
      "title": "Global Weighted Fisher Information Matrix",
      "topics": [
        "global_info_mat"
      ]
    },
    {
      "page": "global_mcmc_estimator",
      "title": "Global MCMC Estimator (Benchmark Method)",
      "topics": [
        "global_mcmc_estimator"
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    },
    {
      "page": "global_mcmc_with_time",
      "title": "Global MCMC Estimator with Computation Time Tracking",
      "topics": [
        "global_mcmc_with_time"
      ]
    },
    {
      "page": "global_posterior_gamma",
      "title": "Global Posterior Probability for Model Indicator",
      "topics": [
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      ]
    },
    {
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      "title": "Global Weighted Quasi Log-Likelihood",
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      "title": "Global Score Vector",
      "topics": [
        "global_score"
      ]
    },
    {
      "page": "init_gamma",
      "title": "Initialize Binary Model Indicator Vector Creates a binary (0/1) indicator vector for model selection. Sets 1 for active (true) covariates and 0 for irrelevant covariates.",
      "topics": [
        "init_gamma"
      ]
    },
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      "topics": [
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      ]
    },
    {
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      "title": "Local Quasi Log-Likelihood for One Block",
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        "local_quasi_ll"
      ]
    },
    {
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      "title": "L2 Penalized Quasi Log-Likelihood",
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    },
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