{ "_id": "6a153cddacfb0bcc41d53fbd", "Package": "rbmi", "Title": "Reference Based Multiple Imputation", "Version": "1.6.0.9000", "Authors@R": "c(\nperson(\"Lukas A.\", \"Widmer\", email = \"lukas_andreas.widmer@novartis.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-1471-3493\")),\nperson(\"Craig\", \"Gower-Page\", email = \"craig.gower-page@novartis.com\", role = c(\"aut\")),\nperson(\"Isaac\", \"Gravestock\", email = \"isaac.gravestock@roche.com\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-0283-2065\")),\nperson(\"Alessandro\", \"Noci\", email = \"alessandro.noci@roche.com\", role = c(\"aut\")),\nperson(\"Marcel\", \"Wolbers\", email = \"marcel.wolbers@roche.com\", role = \"aut\", comment = c(ORCID = \"0000-0003-4915-9015\")),\nperson(\"Daniel\", \"Sabanes Bove\", email = \"daniel.sabanes_bove@rconis.com\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-0176-9239\")),\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", "Language": "en-GB", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://pharmaverse.r-universe.dev", "Date/Publication": "2026-03-20 19:04:42 UTC", "RemoteUrl": "https://github.com/insightsengineering/rbmi", "RemoteRef": "HEAD", "RemoteSha": "895e55273bf478eecd3ee516893895aa64eded89", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 06:18:20 UTC", "User": "root" }, "Author": "Lukas A. 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imputation model using a Bayesian approach", "topics": [ "fit_mcmc" ] }, { "page": "fit_mmrm", "title": "Fit a MMRM model", "topics": [ "fit_mmrm" ] }, { "page": "format_method_descriptions", "title": "Format method descriptions", "topics": [ "format_method_descriptions" ] }, { "page": "generate_data_single", "title": "Generate data for a single group", "topics": [ "generate_data_single" ] }, { "page": "get_bootstrap_stack", "title": "Creates a stack object populated with bootstrapped samples", "topics": [ "get_bootstrap_stack" ] }, { "page": "get_conditional_parameters", "title": "Derive conditional multivariate normal parameters", "topics": [ "get_conditional_parameters" ] }, { "page": "get_delta_template", "title": "Get delta utility variables", "topics": [ "get_delta_template" ] }, { "page": "get_draws_mle", "title": "Fit the base imputation model on bootstrap samples", "topics": [ "get_draws_mle" ] }, { "page": "get_ESS", "title": "Extract the Effective Sample Size (ESS) from a 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"get_visit_distribution_parameters" ] }, { "page": "getStrategies", "title": "Get imputation strategies", "topics": [ "getStrategies" ] }, { "page": "has_class", "title": "Does object have a class ?", "topics": [ "has_class" ] }, { "page": "ife", "title": "if else", "topics": [ "ife" ] }, { "page": "imputation_df", "title": "Create a valid 'imputation_df' object", "topics": [ "imputation_df" ] }, { "page": "imputation_list_df", "title": "List of 'imputation_df's", "topics": [ "imputation_list_df" ] }, { "page": "imputation_list_single", "title": "A collection of 'imputation_singles()' grouped by a single 'subjid' ID", "topics": [ "imputation_list_single" ] }, { "page": "imputation_single", "title": "Create a valid 'imputation_single' object", "topics": [ "imputation_single" ] }, { "page": "impute", "title": "Create imputed datasets", "topics": [ "impute", "impute.condmean", "impute.random" ] }, { "page": "impute_data_individual", "title": "Impute data for a single subject", "topics": [ "impute_data_individual" ] }, { "page": "impute_internal", "title": "Create imputed datasets", "topics": [ "impute_internal" ] }, { "page": "impute_outcome", "title": "Sample outcome value", "topics": [ "impute_outcome" ] }, { "page": "invert", "title": "invert", "topics": [ "invert" ] }, { "page": "invert_indexes", "title": "Invert and derive indexes", "topics": [ "invert_indexes" ] }, { "page": "is_absent", "title": "Is value absent", "topics": [ "is_absent" ] }, { "page": "is_char_fact", "title": "Is character or factor", "topics": [ "is_char_fact" ] }, { "page": "is_char_one", "title": "Is single character", "topics": [ "is_char_one" ] }, { "page": "is_in_rbmi_development", "title": "Is package in development mode?", "topics": [ "is_in_rbmi_development" ] }, { "page": "is_num_char_fact", "title": "Is character, factor or numeric", "topics": [ "is_num_char_fact" ] }, { "page": "locf", "title": "Last Observation Carried Forward", "topics": [ "locf" ] }, { "page": "longDataConstructor", "title": "R6 Class for Storing / Accessing & Sampling Longitudinal Data", "topics": [ "longDataConstructor" ] }, { "page": "ls_design", "title": "Calculate design vector for the lsmeans", "topics": [ "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": 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