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drtmle 1.1.2

drtmle 1.1.1

drtmle 1.1.0

drtmle 1.0.5

As of January 2019: * Version 1.0.5 released on GitHub and CRAN. * Addition of snow and data.table packages to suggests to quell re-occurring warnings and errors on CRAN builds. * Added nSL option for fitting and averaging multiple super learners as part of the estimation procedure. * Added adapt_g option for outcome-adaptive propensity score fitting.

drtmle 1.0.4

As of August 2019: * Added minor touch-ups and link fixes to documentation and vignettes. * Improved how slots are ordered upon being included in the return object.

As of July 2019: * Version 1.0.4.9001 released on GitHub. * Removed dependency on plyr package. * Added option to average over repeated Super Learner fits.

As of December 18, 2018: * Version 1.0.4 released on GitHub and CRAN. * Resolved issues arising from returnModels option when users input nuisance parameters. * Added option to bypass future parallelization calls for easier debugging. * Fixed bugs in standard TMLE implementation – namely, more robust fluctuations and corrected variance estimators.

drtmle 1.0.3

As of July 2, 2018: * Version 1.0.3 released on GitHub and CRAN. * Fixed warnings on CRAN builds.

drtmle 1.0.2

As of February 5, 2018: * Version 1.0.2 released on GitHub and CRAN. * Replaced foreach parallelization with future. * Included more robust Super Learner methods. * Fixed test to pass build with long doubles removed. * Accommodated returning estimated influence functions with drtmle() fit for power users. * Incorporated minor documentation corrections and updates.

As of December 11, 2017: * Version 1.0.2.9000 released on GitHub. * More robust convex combination SuperLearner implemented.

drtmle 1.0.0

As of August 17, 2017: * Version 1.0.0 released on CRAN. * Version 1.0.0.9000 released on GitHub.

As of August 15, 2017: * Version 1.0.0 ready for CRAN release.

drtmle 0.0.1

As of April 05, 2017: * The first public release of this package (v. 0.0.1) is made available on GitHub.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.