{ "_id": "6a10a8ebacfb0bcc41cbdc65", "Package": "MultiObjMatch", "Type": "Package", "Title": "Multi-Objective Matching Algorithm", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Shichao\", \"Han\", email = \"schan21@berkeley.edu\", role = c(\"cre\", \"aut\")),\nperson(\"Samuel D.\", \"Pimentel\", email = \"spi@berkeley.edu\", role = \"aut\"))", "Description": "Matching algorithm based on network-flow structure. Users\nare able to modify the emphasis on three different optimization\ngoals: two different distance measures and the number of\ntreated units left unmatched. The method is proposed by\nPimentel and Kelz (2019) .\nThe 'rrelaxiv' package, which provides an alternative solver\nfor the underlying network flow problems, carries an academic\nlicense and is not available on CRAN, but may be downloaded\nfrom Github at .", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.1.2", "Config/testthat/edition": "3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 18:51:50 UTC", "User": "root" }, "Author": "Shichao Han [cre, aut], Samuel D. 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[ "balanceCosts" ] }, { "page": "build.dist.struct", "title": "An internal helper function that generates the data abstraction for the edge weights of the main network structure.", "topics": [ "build.dist.struct" ] }, { "page": "build.dist.struct_user", "title": "An internal helper function that generates the data abstraction for the edge weights of the main network structure using the distance matrix passed by the user.", "topics": [ "build.dist.struct_user" ] }, { "page": "callrelax", "title": "Call relax on the network", "topics": [ "callrelax" ] }, { "page": "check_representative", "title": "Check the representativeness of matched treated units", "topics": [ "check_representative" ] }, { "page": "combine_dist", "title": "An internal helper function that combines two distance object", "topics": [ "combine_dist" ] }, { "page": "combine_match_result", "title": "Combine two matching result", "topics": [ "combine_match_result" ] }, { "page": "compare_matching", "title": "Generate covariate balance in different matches", "topics": [ "compare_matching" ] }, { "page": "compare_tables", "title": "Summarize covariate balance table", "topics": [ "compare_tables" ] }, { "page": "convert_index", "title": "An internal helper function that translates the matching index in the sorted data frame to the original dataframe's row index", "topics": [ "convert_index" ] }, { "page": "convert_names", "title": "Internal helper function that converts axis name to internal variable name", "topics": [ "convert_names" ] }, { "page": "costSkeleton", "title": "Create cost skeleton", "topics": [ "costSkeleton" ] }, { "page": "data_precheck", "title": "Data precheck: Handle missing data(mean imputation) and remove redundant columns; it also adds an NA column for indicating whether it's missing", "topics": [ "data_precheck" ] }, { "page": "descr.stats_general", "title": "Generate summary statistics for matches", "topics": [ "descr.stats_general" ] }, { "page": "dist_bal_match", "title": "Optimal tradeoffs among distance, exclusion and marginal imbalance", "concept": [ "main matching function" ], "topics": [ "dist_bal_match" ] }, { "page": "distanceFunctionHelper", "title": "Helper function that change input distance matrix", "topics": [ "distanceFunctionHelper" ] }, { "page": "dummy", "title": "This is a modified version of the function \"dummy\" from the R package dummies. Original code Copyright (c) 2011 Decision Patterns.", "topics": [ "dummy" ] }, { "page": "edgelist2ISM", "title": "Change the edgelist to the infinity sparse matrix", "topics": [ "edgelist2ISM" ] }, { "page": "excludeCosts", "title": "Create a skeleton representation of the exclusion edge costs", "topics": [ "excludeCosts" ] }, { "page": "extractEdges", "title": "Extract edges from the network", "topics": [ "extractEdges" ] }, { "page": "extractSupply", "title": "Extract the supply nodes from the net", "topics": [ "extractSupply" ] }, { "page": "filter_match_result", "title": "Filter match result", "topics": [ "filter_match_result" ] }, { "page": "flattenSkeleton", "title": "Turns a skeleton representation of edge costs in a network", "topics": [ "flattenSkeleton" ] }, { "page": "generate_rhos", "title": "Generate rho pairs", "topics": [ "generate_rhos" ] }, { "page": "generateRhoObj", "title": "Penalty and objective values summary", "concept": [ "numerical analysis helper functions" ], "topics": [ "generateRhoObj" ] }, { "page": "get_balance_table", "title": "Generate balance table", "concept": [ "numerical analysis helper functions" ], "topics": [ "get_balance_table" ] }, { "page": "get_five_index", "title": "An internal helper function that gives the index of matching with a wide range of number of treated units left unmatched", "topics": [ "get_five_index" ] }, { "page": "get_pairdist_balance_graph", "title": "Total variation imbalance vs. marginal imbalance", "concept": [ "Graphical helper functions for analysis" ], "topics": [ "get_pairdist_balance_graph" ] }, { "page": "get_pairdist_graph", "title": "Distance vs. exclusion", "concept": [ "Graphical helper functions for analysis" ], "topics": [ "get_pairdist_graph" ] }, { "page": "get_rho_obj", "title": "Penalty and objective values summary", "concept": [ "numerical analysis helper functions" ], "topics": [ "get_rho_obj" ] }, { "page": "get_tv_graph", "title": "Marginal imbalance vs. exclusion", "concept": [ "Graphical helper functions for analysis" ], "topics": [ "get_tv_graph" ] }, { "page": "get_unmatched", "title": "Get unmatched percentage", "concept": [ "numerical analysis helper functions" ], "topics": [ "get_unmatched" ] }, { "page": "getExactOn", "title": "Generate a factor for exact matching.", "topics": [ "getExactOn" ] }, { "page": "getPropensityScore", "title": "Fit propensity scores using logistic regression.", "topics": [ "getPropensityScore" ] }, { "page": "makeInfinitySparseMatrix", "title": "Internal helper to build infinity sparse matrix", "topics": [ "makeInfinitySparseMatrix" ] }, { "page": "makeSparse", "title": "Helper function to mask edges", "topics": [ "makeSparse" ] }, { "page": "matched_data", "title": "Get matched dataframe", "topics": [ "matched_data" ] }, { "page": "matched_index", "title": "An internal helper function that translate the matching index in the sorted data frame to the original dataframe's row index", "topics": [ "matched_index" ] }, { "page": "matrix2cost", "title": "change the distance matrix to cost", "topics": [ "matrix2cost" ] }, { "page": "matrix2edgelist", "title": "Helper function to convert matrix to list", "topics": [ "matrix2edgelist" ] }, { "page": "meldMask", "title": "Helper function to combine two sparse distances", "topics": [ "meldMask" ] }, { "page": "netFlowMatch", "title": "Create network flow structure", "topics": [ "netFlowMatch" ] }, { "page": "obj.to.match", "title": "An internal helper function that transforms the output from the RELAX algorithm to a data structure that is more interpretable for the output of the main matching function", "topics": [ "obj.to.match" ] }, { "page": "pairCosts", "title": "Create a skeleton representation of the edge costs", "topics": [ "pairCosts" ] }, { "page": "rho_proposition", "title": "Generate penalty coefficient pairs", "topics": [ "rho_proposition" ] }, { "page": "solveP", "title": "Solve the network flow problem - 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