{ "_id": "6a61f89535834fefe99ebf37", "Package": "rbmi", "Title": "Reference Based Multiple Imputation", "Version": "1.6.1", "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@bayer.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": "8.0.0", "Config/testthat/edition": "3", "License": "Apache License (>= 2)", "VignetteBuilder": "R.rsp", "Language": "en-GB", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev", "Repository": "https://insightsengineering.r-universe.dev", "Date/Publication": "2026-07-22 16:07:50 UTC", "RemoteUrl": "https://github.com/insightsengineering/rbmi", "RemoteRef": "v1.6.1", "RemoteSha": "cb82c72725dbee38548e72a1a1364dd588fcf609", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-23 11:11:44 UTC", "User": "root" }, "Author": "Lukas A. Widmer [aut, cre] (ORCID:\n),\nCraig Gower-Page [aut],\nIsaac Gravestock [aut] (ORCID: ),\nAlessandro Noci [aut],\nMarcel Wolbers [aut] (ORCID: ),\nDaniel Sabanes Bove [aut] (ORCID:\n),\nF. Hoffmann-La Roche AG [cph, fnd]", "Maintainer": "Lukas A. 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"topics": [ "eval_mmrm" ] }, { "page": "expand", "title": "Expand and fill in missing 'data.frame' rows", "topics": [ "expand", "expand_locf", "fill_locf" ] }, { "page": "extract_covariates", "title": "Extract Variables from string vector", "topics": [ "extract_covariates" ] }, { "page": "extract_data_mnar_as_na", "title": "Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy)", "topics": [ "extract_data_mnar_as_na" ] }, { "page": "extract_draws", "title": "Extract draws from a 'stanfit' object", "topics": [ "extract_draws" ] }, { "page": "extract_imputed_df", "title": "Extract imputed dataset", "topics": [ "extract_imputed_df" ] }, { "page": "extract_imputed_dfs", "title": "Extract imputed datasets", "topics": [ "extract_imputed_dfs" ] }, { "page": "extract_params", "title": "Extract parameters from a MMRM model", "topics": [ "extract_params" ] }, { "page": "fit_mcmc", "title": "Fit the base 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 'stanfit' object", "topics": [ "get_ESS" ] }, { "page": "get_ests_bmlmi", "title": "Von Hippel and Bartlett pooling of BMLMI method", "topics": [ "get_ests_bmlmi" ] }, { "page": "get_example_data", "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": [ 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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": "ls_design", "title": "Calculate design vector for the lsmeans", "topics": [ "ls_design", 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"title": "Bootstrap Pooling via Percentiles", "topics": [ "pool_bootstrap_percentile" ] }, { "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": "pval_percentile", "title": "P-value of percentile bootstrap", "topics": [ "pval_percentile" ] }, { "page": "QR_decomp", "title": "QR decomposition", "topics": [ "QR_decomp" ] }, { "page": "random_effects_expr", "title": "Construct random effects formula", "topics": [ "random_effects_expr" ] }, { "page": "rbmi-settings", "title": "rbmi settings", "topics": [ 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