{ "_id": "6a5177659b92047c28d509c1", "Package": "BSCB", "Title": "Bayesian Simultaneous Credible Bands for Polynomial Regression", "Version": "1.0.0", "Authors@R": "person(\"Fei\", \"Yang\", email = \"fei.yang@manchester.ac.uk\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-3253-976X\"))", "Description": "Provides functions to construct two-sided Bayesian\nsimultaneous credible bands (BSCBs) for the regression curve in\nunivariate polynomial regression over a finite covariate\ninterval. Six methods are implemented, including Normal-Gamma\nconjugate priors (with empirical Bayes, unit-information, and\ng-prior hyperparameter specifications), non-conjugate priors\nfitted via Hamiltonian Monte Carlo (HMC) using 'cmdstanr', and\na non-informative independent Jeffreys prior approach. Also\nincludes functions for computing the empirical simultaneous\ncoverage rate (ESCR) and posterior simultaneous coverage\nprobability (PSCP), enabling performance comparison across\nmethods. The methodology is described in: Yang, F., Han, Y.,\nLiu, W., & Hall, I. (2026). \"Bayesian simultaneous credible\nbands for polynomial regression\"\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Additional_repositories": "https://stan-dev.r-universe.dev", "Config/testthat/edition": "3", "Date": "2026-07-02", "URL": "https://github.com/fannyyang73/BSCB", "Config/instantiate/stan-dir": "src/stan", "BugReports": "https://github.com/fannyyang73/BSCB/issues", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-10 22:46:30 UTC", "User": "root" }, "Author": "Fei Yang [aut, cre] (ORCID:\n)", "Maintainer": "Fei Yang ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-10 20:10:32 UTC", "RemoteUrl": "https://github.com/cran/BSCB", "RemoteRef": "HEAD", "RemoteSha": "f55336bafec48812bddffe176dfc99b6009dd0a7", "_user": "cran", "_type": "src", "_file": "BSCB_1.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/4ea737d17de472867d2497e45b37245b8f535e5e1b1eeb8590eb6460bd48eeec", "_filesize": 636612, "_sha256": "4ea737d17de472867d2497e45b37245b8f535e5e1b1eeb8590eb6460bd48eeec", "_expires": "2026-10-18T22:51:15.000Z", "_created": "2026-07-10T22:46:30.000Z", "_published": "2026-07-10T22:51:17.548Z", "_jobs": [ { "job": 86479525532, "time": 227, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "8241287439" }, { "job": 86479525525, "time": 216, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8241283453" }, { "job": 86479525512, "time": 235, "config": "linux-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8241289147" }, { "job": 86479525576, "time": 240, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8241289737" }, { "job": 86478969146, "time": 253, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8241225536" }, { "job": 86479525505, "time": 171, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8241271278" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/cran/actions/runs/29128452141", "_status": "success", "_upstream": "https://github.com/cran/BSCB", "_commit": { "id": "f55336bafec48812bddffe176dfc99b6009dd0a7", "author": "Fei Yang ", "committer": "cran-robot ", "message": "version 1.0.0\n", "time": 1783714232 }, "_maintainer": { "name": "Fei Yang", "email": "fei.yang@manchester.ac.uk", "orcid": "0000-0002-3253-976X" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.0", "role": "Depends" }, { "package": "mvtnorm", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "OptimalDesign", "role": "Imports" }, { "package": "instantiate", "role": "Imports" }, { "package": "posterior", "role": "Imports" }, { "package": "DEoptim", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "cmdstanr", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": 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"compute_bscb_conjugate", "compute_bscb_hmc", "compute_bscb_ind_jeffreys", "coverage_ESCR", "coverage_PSCP", "create_order_form", "f_L_SCB", "f_U_SCB", "find_global_maximum", "find_global_maximum_h_all", "fn_Bayes_ECR", "fn_Bayes_PCP", "fn_Freq_ECR", "fn_neg_Bayes_ECR", "fn_neg_Bayes_PCP", "generate_simulation_data", "L_SCB", "sup_T_Bayes_ESCR", "sup_T_Bayes_PSCP", "sup_T_Freq_ESCR", "sup_T_simFSCB", "U_SCB" ], "_help": [ { "page": "compute_bpcb_ind_jeffreys", "title": "BPCB-I-J: the Bayesian pointwise credible band using the independent Jeffreys prior", "topics": [ "compute_bpcb_ind_jeffreys" ] }, { "page": "compute_bscb_conjugate", "title": "BSCB-C: Bayesian Simultaneous Credible Band under the Normal-Gamma Conjugate Prior", "topics": [ "compute_bscb_conjugate" ] }, { "page": "compute_bscb_hmc", "title": "Compute BSCB via Hamiltonian Monte Carlo", "topics": [ "compute_bscb_hmc" ] }, { "page": "compute_bscb_ind_jeffreys", "title": "BSCB-J: Bayesian Simultaneous Credible Band under the Independent Jeffreys Prior", "topics": [ "compute_bscb_ind_jeffreys" ] }, { "page": "coverage_ESCR", "title": "Compute the coverage of BSCB", "topics": [ "coverage_ESCR" ] }, { "page": "coverage_PSCP", "title": "Compute the Posterior Simultaneous Coverage Probability (PSCP)", "topics": [ "coverage_PSCP" ] }, { "page": "create_order_form", "title": "Create a polynomial basis vector function", "topics": [ "create_order_form" ] }, { "page": "f_L_SCB", "title": "Vertical distance from true curve to lower band boundary", "topics": [ "f_L_SCB" ] }, { "page": "f_U_SCB", "title": "Vertical distance from true curve to upper band boundary", "topics": [ "f_U_SCB" ] }, { "page": "find_global_maximum", "title": "Find the global maximum of T(x) via grid search and local optimisation", "topics": [ "find_global_maximum" ] }, { "page": "find_global_maximum_h_all", "title": "Find the global maximum of T(x) analytically via polyroot (Liu's method)", "topics": [ "find_global_maximum_h_all" ] }, { "page": "fn_Bayes_ECR", "title": "T(x) for computing ESCR: uses true parameter theta_true", "topics": [ "fn_Bayes_ECR" ] }, { "page": "fn_Bayes_PCP", "title": "T(x) for computing lambda (PSCP): uses posterior draw theta_hat", "topics": [ "fn_Bayes_PCP" ] }, { "page": "fn_Freq_ECR", "title": "T(x) to compute ESCR for frequentist methods", "topics": [ "fn_Freq_ECR" ] }, { "page": "fn_neg_Bayes_ECR", "title": "T(x) for computing ESCR for DEoptim: uses true parameter theta_true", "topics": [ "fn_neg_Bayes_ECR" ] }, { "page": "fn_neg_Bayes_PCP", "title": "Negative T(x) for minimisation-based optimisers (e.g. DEoptim)", "topics": [ "fn_neg_Bayes_PCP" ] }, { "page": "generate_simulation_data", "title": "Generate simulation datasets for polynomial regression", "topics": [ "generate_simulation_data" ] }, { "page": "L_SCB", "title": "Evaluate lower band at x", "topics": [ "L_SCB" ] }, { "page": "sup_T_Bayes_ESCR", "title": "To compute ESCR for BSCB and BPCB For BSCB use 'cov_mat = cov_theta'; for BPCB use 'cov_mat = scale_mat'.", "topics": [ "sup_T_Bayes_ESCR" ] }, { "page": "sup_T_Bayes_PSCP", "title": "To compute the critical constant for BSCB and BPCB; To compute PSCP for BSCB and BPCB", "topics": [ "sup_T_Bayes_PSCP" ] }, { "page": "sup_T_Freq_ESCR", "title": "To compute the ESCR for FSCB and FPCB", "topics": [ "sup_T_Freq_ESCR" ] }, { "page": "sup_T_simFSCB", "title": "To compute the critical constant for simFSCB", "topics": [ "sup_T_simFSCB" ] }, { "page": "U_SCB", "title": "Evaluate upper band at x", "topics": [ "U_SCB" ] } ], "_readme": "https://github.com/cran/BSCB/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "base64enc", "bslib", "cachem", "callr", "checkmate", "cli", "digest", "distributional", "evaluate", "fastmap", "fontawesome", "fs", "generics", "glue", "highr", "htmltools", "htmlwidgets", "instantiate", "jquerylib", "jsonlite", "knitr", "lattice", "lifecycle", "lpSolve", "magrittr", "MASS", "Matrix", "matrixcalc", "matrixStats", "memoise", "mime", 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Simulate Data", "2. Construct the Bands", "BSCB-C: BSCB under the Normal-Gamma conjugate prior", "BSCB-I-J: BSCB under the independent Jeffreys prior", "BSCB-H-C: BSCB under the normal-half-Cauchy prior(0,2) implemented via HMC", "BPCB-I-J: BPCB under the independent Jeffreys prior", "3. Evaluate Bands over a Grid and Plot", "4. 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