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 |