[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-289813-105":59,"doc-detail-289813-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","multiple-imputation-in-causal-graph-discovery-version-112-package-documentation","Multiple Imputation in Causal Graph Discovery - Version 1.1.2 - Package Documentation","","Package micd provides modified and additional R functions to perform constraint-based causal discovery with the PC and FCI algorithms on incomplete data, integrating multiple imputation. It supports bootstrap resampling for missing-value datasets, implements discrete conditional independence testing with test-wise deletion, and supplies wrappers compatible with pcalg workflows. The package documentation details function usage, arguments, returned objects, and illustrative examples for MI-based causal graph construction.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multiple-imputation-in-causal-graph-discovery-version-112-package-documentation/289813/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multiple-imputation-in-causal-graph-discovery-version-112-package-documentation/289813.png","ImageObject",300,407,{"name":92,"@type":93},"Lute","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the micd package address?","Question",{"text":112,"@type":113},"It enables constraint-based causal discovery using PC and FCI when datasets have missing values by combining the algorithms with multiple imputation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does boot.graph handle missing data?",{"text":117,"@type":113},"It generates R bootstrap replicates for the PC or FCI algorithm using missing-value data, driven by arguments such as method and the number of replications.",{"name":119,"@type":110,"acceptedAnswer":120},"What does disCItwd test and how are missing observations handled?",{"text":121,"@type":113},"disCItwd wraps a G-square conditional independence test for discrete variables and performs test-wise deletion by deleting observations where at least one variable involved in the test is missing.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},289813,1789637333,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},137454149569,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Package ‘micd’  \nMay 8, 2026  \nType Package  \nTitle Multiple Imputation in Causal Graph Discovery Version 1.1.2  \nDate 2025-11-21  \nMaintainer Ronja Foraita \u003C[foraita@leibniz-bips.de](foraita@leibniz-bips.de)>  \nDescription Modified functions of the package 'pcalg' and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets.  \nForaita R, Friemel J, Gün  \nther K, Behrens T, BullerdiekJ, Nimzyk R, AhrensW, Didelez V (2020) \u003Cdoi:10.1111/rssa.12565>;  \nAndrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) \u003Cdoi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (20 Depends R (>= 3.0.2), pcalg, mice  \nImports RBGL, Rfast, methods, stats, utils  \nSuggests nnet, ranger, Rgraphviz, testthat (>= 3.0.0)  \nLicense GPL (>= 3) Encoding UTF-8 RoxygenNote 7.3.3  \nURL [https://github.com/bips-hb/micd](https://github.com/bips-hb/micd)  \nBugReports [https://github.com/bips-hb/micd/issues](https://github.com/bips-hb/micd/issues)[ ](https://github.com/bips-hb/micd/issues)Config/testthat/edition 3  \nNeedsCompilation no  \nAuthor Ronja Foraita [aut, cph, cre](ORCID:  \n\u003C[https://orcid.org/0000-0003-2216-6653](https://orcid.org/0000-0003-2216-6653)>),  \nJanine Witte [aut] Repository CRAN  \nDate/Publication 2025-10-23 10:40:14 UTC  \nContents  \nboot.graph .......................................... 2  \ndisCItwd ........................................... 3  \ndisMItest .......................................... 5  \nfciMI ............................................ 6  \nflexCItest .......................................... 8  \nflexCItwd .......................................... 9  \nflexMItest .......................................... 10  \ngaussCItwd ......................................... 12  \ngaussMItest ......................................... 13  \ngetSuff ............................................ 14  \nmake.formulas.saturated .................................. 16  \nmakeResiduals ....................................... 17  \nmixCItest .......................................... 19  \nmixCItwd .......................................... 20  \nmixMItest .......................................... 21  \npcMI ............................................. 23  \nskeletonMI ......................................... 25  \nwith_graph ......................................... 26  \nIndex 28  \n\n| boot.graph | Bootstrap Resampling for the PC-MI-and the FCI-MI-algorithm |\n| --- | --- |\n\nDescription  \nGenerate R bootstrap replicates for the PC or FCI algorithm for data with missing values.  \nUsage  \nboot.graph(  \ndata,  \nselect = NULL,  \nmethod = c(\"pcMI\", \"fciMI\"),  \nmethod.mice = NULL,  \nargs,  \nR,  \nm = 10,  \nargs . residuals = NULL,  \nseed = NA,  \nquickpred = FALSE,  \n...  \n)  \ndisCItwd 3  \nArguments  \ndata Data.frame with missing values  \nselect Variable of integers, indicating columns to select from a data frame; only con  \ntinuous variables can be included in the model selection  \nmethod Character string specifying the algorithm for causal discovery from the package  \n’pcalg’.  \nmethod.mice Character string specifying imputation method; see mice::mice() for more information.  \nargs Arguments passed to method. NOTE: argument labels is set internally and  \nshould not be used!  \nR A positive integer number of bootstrap replications.  \nm Number of chains included in mice()‘.  \nargs . residuals (Optional) list containing vertices and confounders. May be specified when residuals for vertices should be calculated in each bootstrap data set. See makeResiduals() for more information  \nseed A positive integer that is used as argument for set.seed() .  \nquickpred If true, mice uses quickpred to select predictors.  \n... Further arguments passed to the imputation function mice() .  \nValue  \nList of objects of class pcalgo (see pcalg::pcAlgo) or of fcmialgo (see pcalg::fciAlgo) .  \nExamples  \ndata(windspeed)  \ndaten \u003C -mice::ampute(windspeed)$amp  \nbgraph \u003C - boot","cbCaikVbHUi8fpW2","https://ap.wps.com/l/cbCaikVbHUi8fpW2","pdf",146323,28,"English","# boot.graph\n## Function overview\n# disCItwd\n## Wrapper for G-square conditional independence tests","[{\"question\":\"What problem does the micd package address?\",\"answer\":\"It enables constraint-based causal discovery using PC and FCI when datasets have missing values by combining the algorithms with multiple imputation.\"},{\"question\":\"How does boot.graph handle missing data?\",\"answer\":\"It generates R bootstrap replicates for the PC or FCI algorithm using missing-value data, driven by arguments such as method and the number of replications.\"},{\"question\":\"What does disCItwd test and how are missing observations handled?\",\"answer\":\"disCItwd wraps a G-square conditional independence test for discrete variables and performs test-wise deletion by deleting observations where at least one variable involved in the test is missing.\"}]","Multiple Imputation in Causal Graph Discovery - Version 1.1.2 - Package Documentation | PDF",71]