{
  "_id": "6a117271acfb0bcc41cf7d9e",
  "Package": "BayesERtools",
  "Type": "Package",
  "Title": "Bayesian Exposure-Response Analysis Tools",
  "Version": "0.2.5",
  "Authors@R": "c(person(\"Kenta\", \"Yoshida\", , \"yoshida.kenta.6@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-4967-3831\")),\nperson(\"François\", \"Mercier\", , \"francois.mercier@roche.com\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0002-5685-1408\")),\nperson(\"Danielle\", \"Navarro\", , \"djnavarro@protonmail.com\", c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-7648-6578\")),\nperson(\"Genentech, Inc.\", role = \"cph\")\n)",
  "Maintainer": "Kenta Yoshida <yoshida.kenta.6@gmail.com>",
  "Description": "Suite of tools that facilitate exposure-response analysis\nusing Bayesian methods. The package provides a streamlined\nworkflow for fitting types of models that are commonly used in\nexposure-response analysis - linear and Emax for continuous\nendpoints, logistic linear and logistic Emax for binary\nendpoints, as well as performing simulation and visualization.\nLearn more about the workflow at\n<https://genentech.github.io/BayesERbook/>.",
  "License": "Apache License 2.0",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "URL": "https://genentech.github.io/BayesERtools/,\nhttps://genentech.github.io/BayesERbook/",
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  "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libuv1-dev\nlibxml2-dev libssl-dev libnode-dev zlib1g-dev",
  "Repository": "https://genentech.r-universe.dev",
  "Date/Publication": "2026-04-23 16:28:52 UTC",
  "RemoteUrl": "https://github.com/Genentech/BayesERtools",
  "RemoteRef": "HEAD",
  "RemoteSha": "42952a7f35d1938501dd5c89a65f59ebe1cc170b",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-23 09:18:07 UTC",
    "User": "root"
  },
  "Author": "Kenta Yoshida [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4967-3831>),\nFrançois Mercier [aut] (ORCID: <https://orcid.org/0000-0002-5685-1408>),\nDanielle Navarro [aut] (ORCID: <https://orcid.org/0000-0001-7648-6578>),\nGenentech, Inc. [cph]",
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  "_user": "genentech",
  "_type": "src",
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  "_created": "2026-05-23T09:18:07.000Z",
  "_published": "2026-05-23T09:25:04.991Z",
  "_distro": "noble",
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  "_buildurl": "https://github.com/r-universe/genentech/actions/runs/26328980920",
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  "_upstream": "https://github.com/Genentech/BayesERtools",
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    "id": "42952a7f35d1938501dd5c89a65f59ebe1cc170b",
    "author": "Kenta Yoshida <yoshida.kenta@gene.com>",
    "committer": "Kenta Yoshida <yoshida.kenta@gene.com>",
    "message": "CRAN submission prep\n",
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    "name": "Kenta Yoshida",
    "email": "yoshida.kenta.6@gmail.com",
    "login": "yoshidk6",
    "description": "Clinical Pharmacology Modeling & Simulation Scientist (aka Pharmacometrician)",
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    "description": "Dedicated to pursuing groundbreaking science to discover and develop medicines"
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
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  "_homeurl": "https://github.com/genentech/bayesertools",
  "_realowner": "genentech",
  "_cranurl": true,
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  ],
  "_exports": [
    ".dev_ermod_refmodel",
    ".select_cov_projpred",
    "as_draws",
    "as_draws_array",
    "as_draws_df",
    "as_draws_list",
    "as_draws_matrix",
    "as_draws_rvars",
    "build_spec_coveff",
    "build_spec_coveff_one_variable",
    "calc_ersim_med_qi",
    "combine_er_components",
    "dev_ermod_bin",
    "dev_ermod_bin_cov_sel",
    "dev_ermod_bin_emax",
    "dev_ermod_bin_emax_exp_sel",
    "dev_ermod_bin_exp_sel",
    "dev_ermod_emax",
    "dev_ermod_emax_exp_sel",
    "dev_ermod_lin",
    "dev_ermod_lin_cov_sel",
    "dev_ermod_lin_exp_sel",
    "eval_ermod",
    "extract_coef_exp_ci",
    "extract_data",
    "extract_exp_sel_comp",
    "extract_exp_sel_list_model",
    "extract_kfold_loo",
    "extract_mod",
    "extract_var_cov",
    "extract_var_exposure",
    "extract_var_resp",
    "extract_var_selected",
    "kfold",
    "loo",
    "plot_coveff",
    "plot_er",
    "plot_er_exp_sel",
    "plot_er_gof",
    "plot_submod_performance",
    "plot_var_ranking",
    "print_coveff",
    "prior_summary",
    "replace_spec_coveff",
    "sim_coveff",
    "sim_er",
    "sim_er_curve",
    "sim_er_curve_marg",
    "sim_er_new_exp",
    "sim_er_new_exp_marg"
  ],
  "_datasets": [
    {
      "name": "d_sim_binom_cov",
      "title": "Sample simulated data for exposure-response with binary endpoint.",
      "object": "d_sim_binom_cov",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "ID",
        "AETYPE",
        "AEFLAG",
        "Dose_mg",
        "AUCss",
        "Cmaxss",
        "Cminss",
        "BAGE",
        "BWT",
        "BGLUC",
        "BHBA1C",
        "RACE",
        "VISC"
      ],
      "rows": 1500,
      "table": true,
      "tojson": true
    },
    {
      "name": "d_sim_binom_cov_hgly2",
      "title": "Sample simulated data for exposure-response with binary endpoint.",
      "object": "d_sim_binom_cov_hgly2",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "ID",
        "AETYPE",
        "AEFLAG",
        "Dose_mg",
        "AUCss",
        "Cmaxss",
        "Cminss",
        "BAGE",
        "BWT",
        "BGLUC",
        "BHBA1C",
        "RACE",
        "VISC",
        "AUCss_1000",
        "BAGE_10",
        "BWT_10",
        "BHBA1C_5"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    },
    {
      "name": "d_sim_emax",
      "title": "Sample simulated data for Emax exposure-response models with covariates.",
      "object": "d_sim_emax",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "dose",
        "exposure",
        "response_1",
        "response_2",
        "cnt_a",
        "cnt_b",
        "cnt_c",
        "bin_d",
        "bin_e"
      ],
      "rows": 300,
      "table": true,
      "tojson": true
    },
    {
      "name": "d_sim_lin",
      "title": "Sample simulated data for exposure-response with continuous endpoint using linear model.",
      "object": "d_sim_lin",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "ID",
        "AUCss",
        "Cmaxss",
        "BAGE",
        "SEX",
        "response"
      ],
      "rows": 101,
      "table": true,
      "tojson": true
    },
    {
      "name": "d_sim_placebo",
      "title": "Sample simulated data for Emax exposure-response models with covariates and placebo",
      "object": "d_sim_placebo",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "id",
        "dose",
        "exp_1",
        "exp_2",
        "rsp_1",
        "rsp_2",
        "cnt_a",
        "cnt_b",
        "cnt_c",
        "bin_d",
        "bin_e"
      ],
      "rows": 400,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as_draws",
      "title": "Transform to 'draws' objects",
      "topics": [
        "as_draws",
        "as_draws.ermod",
        "as_draws_array",
        "as_draws_array.ermod",
        "as_draws_df",
        "as_draws_df.ermod",
        "as_draws_list",
        "as_draws_list.ermod",
        "as_draws_matrix",
        "as_draws_matrix.ermod",
        "as_draws_rvars",
        "as_draws_rvars.ermod"
      ]
    },
    {
      "page": "build_spec_coveff",
      "title": "Build specifications for covariate effect simulation/visualization",
      "topics": [
        "build_spec_coveff"
      ]
    },
    {
      "page": "calc_ersim_med_qi",
      "title": "Calculate median and quantile intervals from ersim object",
      "topics": [
        "calc_ersim_med_qi"
      ]
    },
    {
      "page": "combine_er_components",
      "title": "Combine ER plot components",
      "topics": [
        "combine_er_components"
      ]
    },
    {
      "page": "d_sim_binom_cov",
      "title": "Sample simulated data for exposure-response with binary endpoint.",
      "topics": [
        "d_sim_binom_cov",
        "d_sim_binom_cov_hgly2"
      ]
    },
    {
      "page": "d_sim_emax",
      "title": "Sample simulated data for Emax exposure-response models with covariates.",
      "topics": [
        "d_sim_emax"
      ]
    },
    {
      "page": "d_sim_lin",
      "title": "Sample simulated data for exposure-response with continuous endpoint using linear model.",
      "topics": [
        "d_sim_lin"
      ]
    },
    {
      "page": "d_sim_placebo",
      "title": "Sample simulated data for Emax exposure-response models with covariates and placebo",
      "topics": [
        "d_sim_placebo"
      ]
    },
    {
      "page": "dev_ermod_bin",
      "title": "Develop linear ER model for binary or continuous endpoint",
      "topics": [
        "dev_ermod_bin",
        "dev_ermod_lin"
      ]
    },
    {
      "page": "dev_ermod_bin_cov_sel",
      "title": "Perform covariate selection for linear ER model",
      "topics": [
        "dev_ermod_bin_cov_sel",
        "dev_ermod_lin_cov_sel"
      ]
    },
    {
      "page": "dev_ermod_bin_exp_sel",
      "title": "Exposure metrics selection for linear ER models",
      "topics": [
        "dev_ermod_bin_exp_sel",
        "dev_ermod_lin_exp_sel"
      ]
    },
    {
      "page": "dev_ermod_emax",
      "title": "Develop Emax model for continuous and binary endpoint",
      "topics": [
        "dev_ermod_bin_emax",
        "dev_ermod_emax"
      ]
    },
    {
      "page": "dev_ermod_emax_exp_sel",
      "title": "Exposure metrics selection for Emax models",
      "topics": [
        "dev_ermod_bin_emax_exp_sel",
        "dev_ermod_emax_exp_sel"
      ]
    },
    {
      "page": "edit_spec_coveff",
      "title": "Customize specifications for covariate effect simulations/visualizations",
      "topics": [
        "build_spec_coveff_one_variable",
        "edit_spec_coveff",
        "replace_spec_coveff"
      ]
    },
    {
      "page": "ermod_cov_sel_method",
      "title": "S3 methods for the classes 'ermod_bin_cov_sel'",
      "topics": [
        "ermod_cov_sel_method",
        "plot.ermod_cov_sel",
        "print.ermod_cov_sel"
      ]
    },
    {
      "page": "ermod_exp_sel_method",
      "title": "S3 methods for the classes 'ermod_exp_sel'",
      "topics": [
        "ermod_exp_sel_method",
        "plot.ermod_exp_sel",
        "print.ermod_exp_sel"
      ]
    },
    {
      "page": "ermod_method",
      "title": "S3 methods for the classes 'ermod_*'",
      "topics": [
        "coef.ermod",
        "ermod_method",
        "plot.ermod_bin",
        "print.ermod",
        "summary.ermod"
      ]
    },
    {
      "page": "ersim_method",
      "title": "S3 methods for the classes ersim_* and ersim_med_qi_*",
      "topics": [
        "ersim_method",
        "plot.ersim",
        "plot.ersim_med_qi"
      ]
    },
    {
      "page": "eval_ermod",
      "title": "Evaluate exposure-response model prediction performance",
      "topics": [
        "eval_ermod"
      ]
    },
    {
      "page": "extract_coef_exp_ci",
      "title": "Extract credible interval of the exposure coefficient",
      "topics": [
        "extract_coef_exp_ci"
      ]
    },
    {
      "page": "extract_method",
      "title": "Extract elements from S3 objects",
      "topics": [
        "extract_data",
        "extract_exp_sel_comp",
        "extract_exp_sel_list_model",
        "extract_method",
        "extract_mod",
        "extract_var_cov",
        "extract_var_exposure",
        "extract_var_resp",
        "extract_var_selected"
      ]
    },
    {
      "page": "kfold",
      "title": "Run k-fold cross-validation",
      "topics": [
        "extract_kfold_loo",
        "kfold",
        "kfold.ermod"
      ]
    },
    {
      "page": "loo",
      "title": "Efficient approximate leave-one-out cross-validation (LOO)",
      "topics": [
        "loo",
        "loo.ermod",
        "loo.ermod_bin_emax",
        "loo.ermod_emax"
      ]
    },
    {
      "page": "p_direction",
      "title": "Probability of Direction (pd)",
      "topics": [
        "p_direction",
        "p_direction.ermod_bin"
      ]
    },
    {
      "page": "plot_cov_sel",
      "title": "Plot variable selection performance",
      "topics": [
        "plot_cov_sel",
        "plot_submod_performance",
        "plot_var_ranking"
      ]
    },
    {
      "page": "plot_coveff",
      "title": "Visualize the covariate effects for ER model",
      "topics": [
        "plot_coveff",
        "plot_coveff.coveffsim",
        "plot_coveff.ermod"
      ]
    },
    {
      "page": "plot_er",
      "title": "Plot ER model simulations",
      "topics": [
        "plot_er",
        "plot_er.ermod",
        "plot_er.ersim",
        "plot_er.ersim_med_qi"
      ]
    },
    {
      "page": "plot_er_exp_sel",
      "title": "Plot exposure metric selection comparison",
      "topics": [
        "plot_er_exp_sel"
      ]
    },
    {
      "page": "plot_er_gof",
      "title": "Default GOF plot for ER model",
      "topics": [
        "plot_er_gof"
      ]
    },
    {
      "page": "print_coveff",
      "title": "Format the covariate effect simulation results for printing",
      "topics": [
        "print_coveff"
      ]
    },
    {
      "page": "prior_summary",
      "title": "Summarize the priors used for linear or linear logistic regression models",
      "topics": [
        "prior_summary",
        "prior_summary.ermod"
      ]
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