biotmle 1.18.0 (BioC 3.14): - Forthcoming. --- biotmle 1.17.0: - Removal of `future` and `doFuture` for simplification of parallelization. All control of parallel computation now done through `BiocParallel`. --- biotmle 1.16.0 (BioC 3.13): - No significant updates. --- biotmle 1.15.0: - No significant updates. --- biotmle 1.14.0 (BioC 3.12): - No significant updates. --- biotmle 1.13.0: - No significant updates. --- biotmle 1.12.0: - No significant updates. --- biotmle 1.11.0 (BioC 3.11): - Change of estimation backend from the `tmle` package to the `drtmle` package. - Removal of option to have repeated subjects since unsupported in new backend. - Adds argument `bppar_debug` to facilitate debugging around parallelization. --- biotmle 1.10.0 (BioC 3.10): - No significant updates. --- biotmle 1.8.0 (BioC 3.9): - No significant updates. --- biotmle 1.6.0 (BioC 3.8): - No significant updates. --- biotmle 1.4.0 (BioC 3.7): - An updated release of this package for Bioconductor 3.7, released April 2018. - This release primarily implements minor changes, including the use of colors in the plots produced by the visualization methods. --- biotmle 1.3.0 (BioC 3.6): - An updated release of this package for Bioconductor 3.6, released in October 2017. - An option for applying this methodology to next-generation sequencing data has been added, based on the popular "voom" transform of the limma R package. - Facilities for parallelized computation have been completely re-implemented: current routines favor a combination of future and BiocParallel. - The method for estimating biomarkers based on an observed outcome has been removed (temporarily). Inference based on this method requires re-thinking. - A full suite of unit tests have been added, covering most package functions. --- biotmle 1.0.0 (BioC 3.5): - The first release of this package was made as part of Bioconductor 3.5, in 2016. --- The biotmle R package provides routines for statistical methodology first described in the technical manuscript [1] and the software paper [2]: 1. Nima S. Hejazi, Sara Kherad-Pajouh, Mark J. van der Laan, Alan E. Hubbard. Variance stabilization of targeted sstimators of causal parameters in high-dimensional settings. https://arxiv.org/abs/1710.05451 2. Nima S. Hejazi, Weixin Cai, Alan E. Hubbard. biotmle: Targeted Learning for Biomarker Discovery. The Journal of Open Source Software, 2(15), 2017. https://dx.doi.org/10.21105/joss.00295