tmleLite-package {tmleLite} | R Documentation |
Targeted maximum likelihood estimation of marginal additive treatment effect of a binary point treatment on a continuous or binary outcome, adjusting for baseline covariates. Missingness in the outcome is accounted for in the estimation procedure. Optional data-adaptive estimation of Q and g portions of the likelihood using the DSA
algorithm is available. This “Lite” version of the software implements a simplified approach to targeted maximum likelihood estimation.
Package: | tmleLite |
Type: | Package |
Version: | 1.0-1 |
Date: | 2009-12-16 |
License: | (none specified) |
LazyLoad: | no |
Susan Gruber, in collaboration with Mark van der Laan.
Maintainer: Susan Gruber, sgruber@berkeley.edu
1. Mark J. van der Laan and Daniel Rubin (2006), “Targeted Maximum Likelihood Learning”. The International Journal of Biostatistics, 2(1). http://www.bepress.com/ijb/vol2/iss1/11/
2. Susan Gruber and Mark J. van der Laan (2009), “Targeted Maximum Likelihood Estimation: A Gentle Introduction”. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 252. http://www.bepress.com/ucbbiostat/paper252
3. Mark J. van der Laan, Sherri Rose, Susan Gruber editors, (2009) “Readings in Targeted Maximum Likelihood Estimation” . U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper xxx. http://www.bepress.com/ucbbiostat
4. Sandra E. Sinisi and Mark J. van der Laan, (2004). “Loss-Based Cross-Validated Deletion/Substitution/Addition Algorithms in Estimation”. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 143. http://www.bepress.com/ucbbiostat/paper143