R: Multi-Objective Matching Algorithm

# Multi-Objective Matching Algorithm

## 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