{ "_id": "6a1166bcacfb0bcc41cf0e1d", "Package": "rbmi", "Title": "Reference Based Multiple Imputation", "Version": "1.6.0", "Authors@R": "c(\nperson(\"Lukas A.\", \"Widmer\", email = \"lukas_andreas.widmer@novartis.com\", role = c(\"aut\", \"cre\")),\nperson(\"Craig\", \"Gower-Page\", email = \"craig.gower-page@novartis.com\", role = c(\"aut\")),\nperson(\"Isaac\", \"Gravestock\", email = \"isaac.gravestock@roche.com\", role = c(\"aut\")),\nperson(\"Alessandro\", \"Noci\", email = \"alessandro.noci@roche.com\", role = c(\"aut\")),\nperson(\"Marcel\", \"Wolbers\", email = \"marcel.wolbers@roche.com\", role = \"ctb\"),\nperson(\"Daniel\", \"Sabanes Bove\", email = \"daniel.sabanes_bove@rconis.com\", role = c(\"aut\")),\nperson(\"F. Hoffmann-La Roche AG\", role = c(\"cph\", \"fnd\"))\n)", "Description": "Implements standard and reference based multiple\nimputation methods for continuous longitudinal endpoints\n(Gower-Page et al. (2022) ). In\nparticular, this package supports deterministic conditional\nmean imputation and jackknifing as described in Wolbers et al.\n(2022) , Bayesian multiple imputation as\ndescribed in Carpenter et al. (2013)\n, and bootstrapped maximum\nlikelihood imputation as described in von Hippel and Bartlett\n(2021) .", "Encoding": "UTF-8", "LazyData": "true", "Roxygen": "list(markdown = TRUE)", "URL": "https://openpharma.github.io/rbmi/,\nhttps://github.com/openpharma/rbmi", "BugReports": "https://github.com/openpharma/rbmi/issues", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "License": "Apache License (>= 2)", "VignetteBuilder": "R.rsp", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://insightsengineering.r-universe.dev", "Date/Publication": "2026-01-23 13:34:48 UTC", "RemoteUrl": "https://github.com/insightsengineering/rbmi", "RemoteRef": "v1.6.0", "RemoteSha": "56a47008855fa3f1abcf55d72eeb5d06e203f73c", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-23 08:30:03 UTC", "User": "root" }, "Author": "Lukas A. 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"title": "Simulate a realistic example dataset", "topics": [ "get_example_data" ] }, { "page": "get_jackknife_stack", "title": "Creates a stack object populated with jackknife samples", "topics": [ "get_jackknife_stack" ] }, { "page": "get_mmrm_sample", "title": "Fit MMRM and returns parameter estimates", "topics": [ "get_mmrm_sample" ] }, { "page": "get_pattern_groups", "title": "Determine patients missingness group", "topics": [ "get_pattern_groups" ] }, { "page": "get_pattern_groups_unique", "title": "Get Pattern Summary", "topics": [ "get_pattern_groups_unique" ] }, { "page": "get_pool_components", "title": "Expected Pool Components", "topics": [ "get_pool_components" ] }, { "page": "get_visit_distribution_parameters", "title": "Derive visit distribution parameters", "topics": [ "get_visit_distribution_parameters" ] }, { "page": "getStrategies", "title": "Get imputation strategies", "topics": [ "getStrategies" ] }, { "page": "has_class", "title": "Does object have a class ?", 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"ls_design", "ls_design_counterfactual", "ls_design_equal", "ls_design_proportional" ] }, { "page": "lsmeans", "title": "Least Square Means", "topics": [ "lsmeans" ] }, { "page": "make_rbmi_cluster", "title": "Create a 'rbmi' ready cluster", "topics": [ "make_rbmi_cluster" ] }, { "page": "mcse_internal", "title": "Internal MCSE Computations", "topics": [ "jackknife_se", "mcse_combine_all_pars", "mcse_internal", "mcse_jackknife" ] }, { "page": "method", "title": "Set the multiple imputation methodology", "topics": [ "method", "method_approxbayes", "method_bayes", "method_bmlmi", "method_condmean" ] }, { "page": "par_lapply", "title": "Parallelise Lapply", "topics": [ "par_lapply" ] }, { "page": "parametric_ci", "title": "Calculate parametric confidence intervals", "topics": [ "parametric_ci" ] }, { "page": "pool", "title": "Pool analysis results obtained from the imputed datasets", "topics": [ "as.data.frame.mcse", "as.data.frame.pool", "mcse", "pool", "print.mcse", "print.pool" ] }, { "page": "pool_bootstrap_normal", "title": "Bootstrap Pooling via normal approximation", "topics": [ "pool_bootstrap_normal" ] }, { "page": "pool_bootstrap_percentile", "title": "Bootstrap Pooling via Percentiles", "topics": [ "pool_bootstrap_percentile" ] }, { "page": "pool_internal", "title": "Internal Pool Methods", "topics": [ "pool_internal", "pool_internal.bmlmi", "pool_internal.bootstrap", "pool_internal.jackknife", "pool_internal.rubin" ] }, { "page": "prepare_stan_data", "title": "Prepare input data to run the Stan model", "topics": [ "prepare_stan_data" ] }, { "page": "print.analysis", "title": "Print 'analysis' object", "topics": [ "print.analysis" ] }, { "page": "print.draws", "title": "Print 'draws' object", "topics": [ "print.draws" ] }, { "page": "print.imputation", "title": "Print 'imputation' object", "topics": [ "print.imputation" ] }, { "page": "progressLogger", "title": "R6 Class for printing current sampling progress", "topics": [ "progressLogger" ] }, { "page": 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