95 lines (64 with data), 2.5 kB

biotmle 1.18.0 (BioC 3.14):
- Forthcoming.

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biotmle 1.17.0:
- Removal of `future` and `doFuture` for simplification of parallelization. All
  control of parallel computation now done through `BiocParallel`.

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biotmle 1.16.0 (BioC 3.13):
- No significant updates.

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biotmle 1.15.0:
- No significant updates.

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biotmle 1.14.0 (BioC 3.12):
- No significant updates.

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biotmle 1.13.0:
- No significant updates.

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biotmle 1.12.0:
- No significant updates.

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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.

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biotmle 1.10.0 (BioC 3.10):
- No significant updates.

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biotmle 1.8.0 (BioC 3.9):
- No significant updates.

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biotmle 1.6.0 (BioC 3.8):
- No significant updates.

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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.

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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.

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biotmle 1.0.0 (BioC 3.5):
- The first release of this package was made as part of Bioconductor 3.5, in
  2016.

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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