Optimal Multilevel Matching using a Network Algorithm

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. |

describe_data_counts | Print out summary of student and school counts |

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 | matchMulti Package |

matchMultiResult | matchMultiResult object for results of power calculations |

matchMultisens | Rosenbaum Bounds after Multilevel Matching |

matchSchools | Match Schools on Student-based Distance |

matchStudents | Compute Student Matches for all Pairs of Schools |

minischool | Mini-data set for illustration |

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 |

tally_schools | Tally schools and students in a given dataset |

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