tmle
swMATH ID:  18463 
Software Authors:  Susan Gruber, Mark van der Laan 
Description:  R package tmle. Targeted Maximum Likelihood Estimation. Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM() function to the package, for estimating the parameters of a marginal structural model for a binary point treatment effect. The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call ’SuperLearner’ to estimate the Q and g portions of the likelihood, unless values or a usersupplied regression function are passed in as arguments. 
Homepage:  https://cran.rproject.org/web/packages/tmle/index.html 
Source Code:  https://github.com/cran/tmle 
Dependencies:  R 
Keywords:  CRAN; R package; Targeted Maximum Likelihood Estimation; TMLE 
Related Software:  SuperLearner; R; gam; ltmle; caret; simcausal; arm; glmnet; BART; drtmle; EValue; episensr; optmatch; Matching; MatchIt; sbw; optweight; PSW; causalweight; CBPS 
Cited in:  6 Publications 
Standard Articles
1 Publication describing the Software  Year 


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top 5
Cited by 15 Authors
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top 5
Cited in 6 Serials
1  Psychometrika 
1  Biometrics 
1  Communications in Statistics. Simulation and Computation 
1  Statistical Papers 
1  Journal of Statistical Theory and Practice 
1  Springer Series in Statistics 
Cited in 2 Fields
6  Statistics (62XX) 
3  Computer science (68XX) 