Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.
|Author||Luke Keele and Sam Pimentel|
|Date of publication||2016-08-26 07:35:43|
|Maintainer||Sam Pimentel <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
agg: Extract School-Level Covariates
assembleMatch: Collect Matched Samples
balanceMulti: Performs balance checking after multilevel matching.
balanceTable: Create Balance Table
buildCaliper: Construct propensity score caliper
catholic_schools: 1980 and 1982 High School and Beyond Data
ci_func: Outcome analysis.
handleNA: Handle Missing Values
is.binary: Check if a variable is binary
match2distance: Compute School Distance from a Student Match
matchMulti: A function that performs multilevel matching.
matchMultioutcome: Performs an outcome analysis after multilevel matching.
matchMulti-package: Optimal Multilevel Matching using a Network Algorithm
matchMultisens: Rosenbaum Bounds after Multilevel Matching
matchSchools: Match Schools on Student-based Distance
matchStudents: Compute Student Matches for all Pairs of Schools
pairmatchelastic: Optimal Subset Matching without Balance Constraints
pe_func: Outcome analysis.
pval_func: Outcome analysis.
rematchSchools: Repeat School Match Only
resolve.cols: Ensure Dataframes Share Same Set Columns
sdiff: Balance Measures
smahal: Robust Mahalanobis Distance
students2schools: Aggregate Student Data into School Data