--- a
+++ b/inst/NEWS
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+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
+