Optimal Multilevel Matching using a Network Algorithm
Outcome analysis.
Repeat School Match Only
Ensure Dataframes Share Same Set Columns
Extract School-Level Covariates
Collect Matched Samples
Performs balance checking after multilevel matching.
Create Balance Table
Construct propensity score caliper
Outcome analysis.
Print out summary of student and school counts
Handle Missing Values
Check if a variable is binary
Compute School Distance from a Student Match
matchMulti Package
A function that performs multilevel matching.
Performs an outcome analysis after multilevel matching.
matchMultiResult object for results of power calculations
Rosenbaum Bounds after Multilevel Matching
Match Schools on Student-based Distance
Compute Student Matches for all Pairs of Schools
Optimal Subset Matching without Balance Constraints
Outcome analysis.
Balance Measures
Robust Mahalanobis Distance
Aggregate Student Data into School Data
Tally schools and students in a given dataset
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. Details in Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>. The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at <https://github.com/markmfredrickson/optmatch> if not available on CRAN.