Multi-Objective Matching Algorithm

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Documentation for package ‘MultiObjMatch’ version 1.0.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

addBalance	Add fine balance edges
addExclusion	Add exclusion edges
balanceCosts	Create a skeleton representation of the balance edge costs
build.dist.struct	An internal helper function that generates the data abstraction for the edge weights of the main network structure.
build.dist.struct_user	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.
callrelax	Call relax on the network
check_representative	Check the representativeness of matched treated units
combine_dist	An internal helper function that combines two distance object
combine_match_result	Combine two matching result
compare_matching	Generate covariate balance in different matches
compare_tables	Summarize covariate balance table
convert_index	An internal helper function that translates the matching index in the sorted data frame to the original dataframe's row index
convert_names	Internal helper function that converts axis name to internal variable name
costSkeleton	Create cost skeleton
data_precheck	Data precheck: Handle missing data(mean imputation) and remove redundant columns; it also adds an NA column for indicating whether it's missing
descr.stats_general	Generate summary statistics for matches
distanceFunctionHelper	Helper function that change input distance matrix
dist_bal_match	Optimal tradeoffs among distance, exclusion and marginal imbalance
dummy	This is a modified version of the function "dummy" from the R package dummies. Original code Copyright (c) 2011 Decision Patterns.
edgelist2ISM	Change the edgelist to the infinity sparse matrix
excludeCosts	Create a skeleton representation of the exclusion edge costs
extractEdges	Extract edges from the network
extractSupply	Extract the supply nodes from the net
filter_match_result	Filter match result
flattenSkeleton	Turns a skeleton representation of edge costs in a network
generateRhoObj	Penalty and objective values summary
generate_rhos	Generate rho pairs
getExactOn	Generate a factor for exact matching.
getPropensityScore	Fit propensity scores using logistic regression.
get_balance_table	Generate balance table
get_five_index	An internal helper function that gives the index of matching with a wide range of number of treated units left unmatched
get_pairdist_balance_graph	Total variation imbalance vs. marginal imbalance
get_pairdist_graph	Distance vs. exclusion
get_rho_obj	Penalty and objective values summary
get_tv_graph	Marginal imbalance vs. exclusion
get_unmatched	Get unmatched percentage
makeInfinitySparseMatrix	Internal helper to build infinity sparse matrix
makeSparse	Helper function to mask edges
matched_data	Get matched dataframe
matched_index	An internal helper function that translate the matching index in the sorted data frame to the original dataframe's row index
matrix2cost	change the distance matrix to cost
matrix2edgelist	Helper function to convert matrix to list
meldMask	Helper function to combine two sparse distances
netFlowMatch	Create network flow structure
obj.to.match	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
pairCosts	Create a skeleton representation of the edge costs
rho_proposition	Generate penalty coefficient pairs
solveP	Solve the network flow problem - basic version
solveP1	Solve the network flow problem - twoDistMatch
summary.multiObjMatch	Generate numerical summary
two_dist_match	Optimal tradeoffs among two distances and exclusion
visualize	Visualize tradeoffs