NEWS
biotmle 1.18.0
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
biotmle 1.15.0
biotmle 1.14.0
biotmle 1.13.0
biotmle 1.12.0
biotmle 1.11.0
- 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
biotmle 1.8.0
biotmle 1.6.0
biotmle 1.4.0
- 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
- 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
- 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