CRAN

multilevelMatching 1.0.0

Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments

Released May 8, 2019 by Brian G. Barkley

Dependencies

nnet 7.3-12 MASS 7.3-51.4 boot 1.3-22 Matching 4.9-6

Implements methods to estimate causal effects from observational studies when there are 2+ distinct levels of treatment (i.e., "multilevel treatment") using matching estimators, as introduced in Yang et al. (2016) . Matching on covariates, and matching or stratification on modeled propensity scores, are available. These methods require matching on only a scalar function of generalized propensity scores.

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>multilevelMatching</artifactId>
    <version>1.0.0-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:multilevelMatching')

Test Results

This package was last tested against Renjin 0.9.2726 on May 10, 2019.

• calcKMVarFactor-examples
• estimateTrtModel-examples
• multiMatch-examples
• multilevelGPSMatch-examples
• multilevelGPSStratification-examples
• multilevelMatchX-examples
• multilevelMatching-examples
• nameContrast-examples
• nameMu-examples
• prepareData-examples
• print.multiMatch-examples
• summary.multiMatch-examples
• testthat
• toy_dataset_results.multiMatch()_returns_same_as_multilevelMatchX()_on_one_covariate
• toy_dataset_results.multilevelGPSMatch()_one_X_and_trimming_returns_same_output_as_v0_1
• toy_dataset_results.multilevelGPSMatch()_with_one_X,_no_tri,_returns_same_output_as_v0_1
• toy_dataset_results.multilevelMatchX()_on_one_X_returns_same_output_as_v0_1_E1
• toy_dataset_results.multilevelMatchX()_on_one_X_returns_same_output_as_v0_1_E2
• toy_dataset_results.multilevelMatchX()_with_one-column_matrix_X_returns_same_output_as_v0_1

Source

View GitHub Mirror

Release History

• 1.0.0