earth
dependency to fix build errorsstratify
argument changed to FALSE
future
parallel backend must be specified externally by userAs 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.
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.
As of July 2, 2018: Version 1.0.3 released on GitHub and CRAN. Fixed warnings on CRAN builds.
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.
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.
As of April 05, 2017: * The first public release of this package (v. 0.0.1) is made available on GitHub.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.