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R packages for matching:

  •  multilevelMatching for implementing a novel matching procedure to compare multiple treatments simultaneously from the observational data. (CRAN)
  • dsmatch implements double score matching for the average treatment effect and quantile treatment effect estimation.


R packags for continuous-time causal inference

  • contTimeCausal provides estimation methods for continuous-time structural failure time models (ctSFTM) and continuous-time Cox marginal structural models (ctCoxMSM). (CRAN)


R packages for generalizing randomized trial findings

  • genRCT generalizes the average treatment effect (ATE) from the trial using observational studies (OS) for binary/continuous/survival outcomes.


R packages for integrative analysis

  • IntegrativeFPM implements integrative analyses for the finite population mean combining probability and non-probability samples with high-dimensional data.
  • IntegrativeCI implements integrative analyses for the average treatment effect combining big main data and smaller validation data.
  • IntegrativeHTE implements elastic analyses for the heterogeneous treatment effects combining trials and real-world data.
  • IntegrativeHTEcf implements integrative analyses for the heterogeneous treatment effects combining a randomized trial and confounded real-world data.


R packages or functions for missing data, causal inference, and individualized treatment regime learning

  • IITR implements a doubly robust estimator of the risk function and Adpative LASSO based algorithm to find interpretable individualized treatment regime.
  • miATE implements a unified bootstrap inference of the average treatment effect after multiple imputation based on martingales.
  • pace implements various estimators of principal strata average causal effects from observational studies.
  • smim implements Survival sensitivity analysis using Multiple Imputation and Martingale.
  • spatial_confounding contains R-scripts and other files that were employed to carry out simulation studies and data analysis described in the paper “A Spectral Adjustment for Spatial Confounding.”
  • MSNMM solves the doubly robust estimating equation proposed in the paper “Multiplicative structural nested mean model for zero-inflated outcomes.”