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lmtp R package by Nick Williams implementing the methods we proposed here. Some features are:
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Estimation of causal effects for static interventions and modified treatment policies in longitudinal data
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Continuous, categorical, and binary exposures
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Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters
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Right-censored data
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adjrct R package by Nick Williams implementing the methods we proposed here and in references therein. Some features are:
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Estimation of covariate adjusted effects for randomized trials, with efficiency guarantees
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Estimation of effects for ordinal and time-to-event endpoints
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Incorporation of machine learning for maximum efficiency gains
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medshift R package by Nima Hejazi:
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Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters
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Mediation analysis for stochastic interventions
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Continuous exposures
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Multivariate mediators
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medoutcon R package by Nima Hejazi:
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Incorporates Super Learner sl3 ensemble learners for flexible machine learning estimation of nuisance parameters
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Mediation analysis for binary exposures in the presence of intermediate confounders
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Multivariate mediators
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