{ "_id": "6a46dcf66527f6f71f9f795f", "Package": "powerbrmsINLA", "Title": "Bayesian Power Analysis Using 'brms' and 'INLA'", "Version": "1.3.0", "Maintainer": "Tony Myers ", "Authors@R": "person(given = \"Tony\",\nfamily = \"Myers\",\nrole = c(\"aut\", \"cre\"),\nemail = \"admyers@aol.com\",\ncomment = c(ORCID = \"0000-0003-4516-4829\"))", "Description": "Provides tools for Bayesian power analysis and assurance\ncalculations using the statistical frameworks of 'brms' and\n'INLA'. Includes simulation-based approaches, support for\nmultiple decision rules (direction, threshold, ROPE),\nsequential designs, and visualisation helpers. Methods are\nbased on Kruschke (2014, ISBN:9780124058880) \"Doing Bayesian\nData Analysis: A Tutorial with R, JAGS, and Stan\", O'Hagan &\nStevens (2001) \"Bayesian\nAssessment of Sample Size for Clinical Trials of\nCost-Effectiveness\", Kruschke (2018)\n \"Rejecting or Accepting\nParameter Values in Bayesian Estimation\", Rue et al. (2009)\n \"Approximate Bayesian\ninference for latent Gaussian models by using integrated nested\nLaplace approximations\", and Bürkner (2017)\n \"brms: An R Package for Bayesian\nMultilevel Models using Stan\".", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "URL": "https://github.com/Tony-Myers/powerbrmsINLA", "BugReports": "https://github.com/Tony-Myers/powerbrmsINLA/issues", "Additional_repositories": "https://inla.r-inla-download.org/R/stable", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-02 21:44:27 UTC", "User": "root" }, "Author": "Tony Myers [aut, cre] (ORCID:\n)", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-07-02 10:40:02 UTC", "RemoteUrl": "https://github.com/cran/powerbrmsINLA", "RemoteRef": "HEAD", "RemoteSha": "88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b", "_user": "cran", "_type": "src", "_file": "powerbrmsINLA_1.3.0.tar.gz", "_fileid": "https://r2.ropensci.org/5e01ffd95cc37735775c1d70794be690c981cabfaaa0d084ade9e8555c56c623", "_filesize": 406277, "_sha256": "5e01ffd95cc37735775c1d70794be690c981cabfaaa0d084ade9e8555c56c623", "_expires": "2026-10-10T21:49:41.000Z", "_created": "2026-07-02T21:44:27.000Z", "_published": "2026-07-02T21:49:42.104Z", "_jobs": [ { "job": 84884837635, "time": 244, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8052448798" }, { "job": 84884837627, "time": 234, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8052445789" }, { "job": 84884192022, "time": 248, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8052360037" }, { "job": 84884837647, "time": 197, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8052432515" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/cran/actions/runs/28623347563", "_status": "success", "_upstream": "https://github.com/cran/powerbrmsINLA", "_commit": { "id": "88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b", "author": "Tony Myers ", "committer": "cran-robot ", "message": "version 1.3.0\n", "time": 1782988802 }, "_maintainer": { "name": "Tony Myers", "email": "admyers@aol.com", "login": "tony-myers", "description": "Professor in Quantitative Methods, Birmingham Newman University, UK: https://www.newman.ac.uk/staff/david-anthony-myers/", "uuid": 40142137, "orcid": "0000-0003-4516-4829" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "brms", "version": ">= 2.19.0", "role": "Imports" }, { "package": "dplyr", "version": ">= 1.1.0", "role": "Imports" }, { "package": "ggplot2", "version": ">= 3.4.0", "role": "Imports" }, { "package": "pbapply", "role": "Imports" }, { "package": "rlang", "version": ">= 1.1.0", "role": "Imports" }, { "package": "tibble", "version": ">= 3.2.0", "role": "Imports" }, { "package": "scales", "version": ">= 1.2.0", "role": "Imports" }, { "package": "viridisLite", "version": ">= 0.4.0", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "tools", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "magrittr", "version": ">= 2.0.0", "role": "Imports" }, { "package": "INLA", "version": ">= 22.05.07", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "circular", "role": "Suggests" }, { "package": "sn", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2025-36", "n": 1 }, { "week": "2025-46", "n": 1 }, { "week": "2026-23", "n": 1 }, { "week": "2026-27", "n": 1 } ], "_tags": [ { "name": "1.0.0", "date": "2025-09-01" }, { "name": "1.1.1", "date": "2025-11-16" }, { "name": "1.2.0", "date": "2026-06-02" }, { "name": "1.3.0", "date": "2026-07-02" } ], "_stars": 0, "_contributors": [ { "user": "tony-myers", "count": 4, "uuid": 40142137 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 615, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 459, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/powerbrmsINLA" }, "_devurl": "https://github.com/tony-myers/powerbrmsinla", "_searchresults": 6, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/powerbrmsINLA.html", "extra/readme.html", "extra/readme.md", "LICENSE", "manual.pdf" ], "_homeurl": "https://github.com/tony-myers/powerbrmsinla", "_realowner": "tony-myers", "_cranurl": false, "_releases": [ { "version": "1.0.0", "date": "2025-09-01" }, { "version": "1.1.1", "date": "2025-11-16" }, { "version": "1.2.0", "date": "2026-06-02" }, { "version": "1.3.0", "date": "2026-07-02" } ], "_exports": [ "%||%", "add_decision_overlay", "assurance_prior_weights", "beta_binom_power", "beta_weights_on_grid", "brms_inla_power", "brms_inla_power_design", "brms_inla_power_parallel", "brms_inla_power_sequential", "brms_inla_power_two_stage", "brms_inla_sequential_trial", "compute_assurance", "decide_sample_size", "hdi_of_icdf", "min_n_beta_binom", "plot_assurance_curve", "plot_assurance_with_robustness", "plot_bf_assurance_curve", "plot_bf_assurance_curve_smooth", "plot_bf_expected_evidence", "plot_bf_heatmap", "plot_decision_assurance_curve", "plot_decision_threshold_contour", "plot_design_prior", "plot_interaction_surface", "plot_power_assurance_overlay", "plot_power_contour", "plot_power_heatmap", "plot_precision_assurance_curve", "plot_precision_fan_chart", "plot_sequential_monitor", "sequential_analysis", "sequential_design", "validate_inla_vs_brms", "validate_sd_spec" ], "_help": [ { "page": "add_decision_overlay", "title": "Add sample-size decision overlay to a conditional power contour", "topics": [ "add_decision_overlay" ] }, { "page": "assurance_prior_weights", "title": "Create prior weights over an effect grid for use with compute_assurance()", "topics": [ "assurance_prior_weights" ] }, { "page": "beta_binom_power", "title": "Analytic Assurance for Beta-Binomial Designs", "topics": [ "beta_binom_power" ] }, { "page": "beta_weights_on_grid", "title": "Beta-Prior Weights Over an Effect Grid", "topics": [ "beta_weights_on_grid" ] }, { "page": "brms_inla_power", "title": "Core Bayesian Assurance / Power Simulation (Modern, Multi-Effect Ready)", "topics": [ "brms_inla_power" ] }, { "page": "brms_inla_power_design", "title": "Design-based wrapper for Bayesian power / assurance", "topics": [ "brms_inla_power_design" ] }, { "page": "brms_inla_power_parallel", "title": "Parallel wrapper for fixed-design Bayesian power / assurance simulations", "topics": [ "brms_inla_power_parallel" ] }, { "page": "brms_inla_power_sequential", "title": "Sequential Bayesian Assurance Simulation Engine (Modern, Multi-Effect Ready)", "topics": [ "brms_inla_power_sequential" ] }, { "page": "brms_inla_power_two_stage", "title": "Two-Stage Bayesian Assurance Simulation (Multi-Effect, User-Friendly API)", "topics": [ "brms_inla_power_two_stage" ] }, { "page": "brms_inla_sequential_trial", "title": "Simulate a sequential Bayesian trial with interim stopping rules", "topics": [ "brms_inla_sequential_trial" ] }, { "page": "compute_assurance", "title": "Compute unconditional Bayesian assurance from simulation results", "topics": [ "compute_assurance" ] }, { "page": "decide_sample_size", "title": "Decide recommended sample size from power/assurance results", "topics": [ "decide_sample_size" ] }, { "page": "hdi_of_icdf", "title": "Highest Density Interval from an Inverse CDF", "topics": [ "hdi_of_icdf" ] }, { "page": "min_n_beta_binom", "title": "Minimum n for Target Assurance (Beta-Binomial)", "topics": [ "min_n_beta_binom" ] }, { "page": "plot_assurance_curve", "title": "Plot Assurance Curve(s) vs Sample Size", "topics": [ "plot_assurance_curve" ] }, { "page": "plot_assurance_with_robustness", "title": "Plot Conditional Power with Robustness Ribbon (Multi-Effect Grid Friendly)", "topics": [ "plot_assurance_with_robustness" ] }, { "page": "plot_bf_assurance_curve", "title": "Bayes-factor conditional power curve (user-facing wrapper)", "topics": [ "plot_bf_assurance_curve" ] }, { "page": "plot_bf_assurance_curve_smooth", "title": "Conditional Bayesian power curve for the Bayes factor criterion with Wilson CIs (multi-effect grid friendly)", "topics": [ "plot_bf_assurance_curve_smooth" ] }, { "page": "plot_bf_expected_evidence", "title": "Plot Expected Evidence (mean log10 BF10, Multi-Effect Grid Friendly)", "topics": [ "plot_bf_expected_evidence" ] }, { "page": "plot_bf_heatmap", "title": "Plot Bayes Factor Heatmap (mean log10 BF10, Multi-Effect Grid Friendly)", "topics": [ "plot_bf_heatmap" ] }, { "page": "plot_decision_assurance_curve", "title": "Plot Conditional Power Curve for a Decision Rule (Multi-Effect Grid Friendly)", "topics": [ "plot_decision_assurance_curve" ] }, { "page": "plot_decision_threshold_contour", "title": "Plot Decision Threshold Contour (Multi-Effect Grid Friendly)", "topics": [ "plot_decision_threshold_contour" ] }, { "page": "plot_design_prior", "title": "Plot Design Prior Density over the Effect Grid", "topics": [ "plot_design_prior" ] }, { "page": "plot_interaction_surface", "title": "Plot Interaction Conditional Power Surface/Heatmap/Lines (Multi-Effect Grid Friendly)", "topics": [ "plot_interaction_surface" ] }, { "page": "plot_power_assurance_overlay", "title": "Plot Conditional Power Curves with Assurance Overlay", "topics": [ "plot_power_assurance_overlay" ] }, { "page": "plot_power_contour", "title": "Draw a filled contour plot of conditional Bayesian power for a chosen metric, as a function of two effect grid columns and sample size.", "topics": [ "plot_power_contour" ] }, { "page": "plot_power_heatmap", "title": "Plot Conditional Bayesian Power Heatmap (Multi-Effect Grid Friendly)", "topics": [ "plot_power_heatmap" ] }, { "page": "plot_precision_assurance_curve", "title": "Plot Precision Conditional Power Curve (Multi-Effect Grid Friendly)", "topics": [ "plot_precision_assurance_curve" ] }, { "page": "plot_precision_fan_chart", "title": "Precision conditional power as a function of sample size", "topics": [ "plot_precision_fan_chart" ] }, { "page": "plot_sequential_monitor", "title": "Plot a sequential analysis trajectory with stopping boundaries", "topics": [ "plot_sequential_monitor" ] }, { "page": "print.brms_inla_power", "title": "Print method for brms_inla_power result objects", "topics": [ "print.brms_inla_power" ] }, { "page": "print.powerbrmsINLA_assurance", "title": "Print method for powerbrmsINLA_assurance objects", "topics": [ "print.powerbrmsINLA_assurance" ] }, { "page": "print.powerbrmsINLA_sample_size", "title": "Print method for powerbrmsINLA_sample_size objects", "topics": [ "print.powerbrmsINLA_sample_size" ] }, { "page": "print.powerbrmsINLA_seq_design", "title": "Print method for sequential design objects", "topics": [ "print.powerbrmsINLA_seq_design" ] }, { "page": "print.powerbrmsINLA_seq_monitor", "title": "Print method for sequential analysis monitor objects", "topics": [ "print.powerbrmsINLA_seq_monitor" ] }, { "page": "print.powerbrmsINLA_seq_trial", "title": "Print method for sequential trial simulation results", "topics": [ "print.powerbrmsINLA_seq_trial" ] }, { "page": "sequential_analysis", "title": "Analyse accumulated real data at a sequential interim look", "topics": [ "sequential_analysis" ] }, { "page": "sequential_design", "title": "Prespecify a sequential Bayesian analysis design", "topics": [ "sequential_design" ] }, { "page": "validate_inla_vs_brms", "title": "Spot-check INLA posterior estimates against brms/Stan", "topics": [ "validate_inla_vs_brms" ] }, { "page": "validate_sd_spec", "title": "Validate an SD Specification for error_sd or group_sd", "topics": [ "validate_sd_spec" ] } ], "_readme": "https://github.com/cran/powerbrmsINLA/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "bayesplot", "BH", "bridgesampling", "brms", "Brobdingnag", "callr", "checkmate", "cli", "coda", "codetools", "cpp11", "desc", "digest", "distributional", "dplyr", "farver", "future", "future.apply", "generics", "ggplot2", "ggridges", "globals", "glue", "gridExtra", "gtable", "inline", "isoband", "labeling", "lattice", "lifecycle", "listenv", "loo", "magrittr", "Matrix", "matrixStats", "mgcv", "mvtnorm", "nleqslv", "nlme", "numDeriv", "otel", "parallelly", "pbapply", "pillar", "pkgbuild", "pkgconfig", "plyr", "posterior", "processx", "ps", "purrr", "QuickJSR", "R6", "RColorBrewer", "Rcpp", "RcppEigen", "RcppParallel", "reshape2", "rlang", "rstan", "rstantools", "S7", "scales", "StanHeaders", "stringi", "stringr", "tensorA", "tibble", "tidyr", "tidyselect", "utf8", "vctrs", "viridisLite", "withr" ], "_vignettes": [ { "source": "validation.Rmd", "filename": "validation.html", "title": "Validation against classical power and Bayesian assurance", "engine": "knitr::rmarkdown", "headings": [ "1 Introduction", "2 Test case 1 — Classical power recovery (Gaussian two-sample design)", "Setup", "Results table", "3 Test case 2 — Chen et al. (2018) Table 1", "powerbrmsINLA comparison", "Comparison table", "4 Test case 3 — Comparison with bayesassurance", "5 Discussion" ], "created": "2026-06-02 16:58:53", "modified": "2026-06-02 16:58:53", "commits": 1 } ], "_score": 3.3010299956639813, "_indexed": false, "_nocasepkg": "powerbrmsinla", "_universes": [ "cran" ], "_indexurl": "https://tony-myers.r-universe.dev/powerbrmsINLA", "_previous": "1.2.0", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.3.0", "date": "2026-07-02T21:48:16.000Z", "distro": "resolute", "commit": "88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b", "fileid": "https://r2.ropensci.org/5baf1ba692670e3fe1f07e5a420d46c9b50a8f7ec225bb3aa1ec505bb16b10cb", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/28623347563" }, { "r": "4.6.1", "os": "linux", "version": "1.3.0", "date": "2026-07-02T21:48:04.000Z", "distro": "resolute", "commit": "88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b", "fileid": "https://r2.ropensci.org/7cc72b206d3dd8311b1fb79da268c758331c75bd29f6373a85ddb6b975151798", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/28623347563" }, { "r": "4.6.0", "os": "wasm", "version": "1.3.0", "date": "2026-07-02T21:48:35.000Z", "commit": "88fcb54eff6ad4f8635ce529fe69e4f33f6d7c0b", "fileid": "https://r2.ropensci.org/3c38a3f09dc3e77b837971836b344a09294bceb7c8cfee09750461388199bc69", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/28623347563" } ] }