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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.
- AMW implements augmented matching weighted estimator for the average treatment effects, which selects the number for matches adaptively and achieves double robustness.
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)
- FPCA implements functional principal component analysis with informative irregularly-spaced observation times.
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.
- genITR conducts transfer learning of the individual treatment regime (ITR) from a source to a target population using summary statistics.
- TansferLearningSurvITR conducts transfer learning of the ITR from a source to a target population for survival outcomes.
R packages for integrative analysis
- 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 using the notion of confounding function.
- intRlearner implements an integrative R-learning analysis of the heterogenous treatment effects by combing a randomized trial and confounded real-world data using the notation of confounding function.
- IntegrativeFPM implements integrative analyses for the finite population mean combining probability and non-probability samples with high-dimensional 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.”
- RealWorld-DoublyRobustML implements various doubly robust estimators using machine learned nuisance functions, including DSM, AIPW, TMLE, and PENCOMP