Doubly-Robust Nonparametric Estimation and Inference
person("David", "Benkeser", email = "benkeser@emory.edu", role = c("aut", "cre","cph"))
Targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality (van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
testthat, knitr, rmarkdown, gam, quadprog, nloptr, doParallel

c(
person("David", "Benkeser", email = "benkeser@emory.edu",
role = c("aut", "cre","cph"),
comment = c(ORCID = "0000-0002-1019-8343")),
person("Nima", "Hejazi", email = "nhejazi@berkeley.edu",
role = "ctb",
comment = c(ORCID = "0000-0002-7127-2789")))
Targeted minimum loss-based estimators of counterfactual means and
causal effects that are doubly-robust with respect both to consistency and
asymptotic normality (Benkeser et al (2017), <doi:10.1093/biomet/asx053>; MJ
van der Laan (2014), <doi:10.1515/ijb-2012-0038>).
SuperLearner, plyr, np, future, doFuture, future.batchtools
testthat, knitr, rmarkdown, gam, quadprog, nloptr, parallel,
snow

SuperLearner, plyr, np, future, doFuture, future.apply,
future.batchtools
testthat, knitr, rmarkdown, gam, earth, quadprog, nloptr,
parallel, snow, foreach

testthat, knitr, rmarkdown, gam, earth, quadprog, nloptr,
parallel, foreach

SuperLearner, np, future, doFuture, future.apply,
future.batchtools
testthat, knitr, rmarkdown, gam, earth, quadprog, nloptr,
parallel, foreach, stringi

testthat, knitr, rmarkdown, gam, earth, quadprog, nloptr,
parallel, foreach, stringi, future.batchtools

testthat, knitr, rmarkdown, gam, quadprog, nloptr, parallel,
foreach, stringi

Drug Interaction Modeling Based on Loewe Additivity Following
Harbron's Approach
Maxim Nazarov, Nele Goeyvaerts, Chris Harbron (original article and code)
Perform assessment of synergy/antagonism for drug combinations based
on the Loewe additivity model, following Harbron's approach (Statistics in
Medicine, 2010, <doi:10.1002/sim.3916>). The package allows flexible modeling
of the drug interaction index and supports "2-stage" estimation in addition
to "1-stage" estimation, including bootstrap-based confidence intervals.
The method requires data on the monotherapy responses and the package
accommodates both checkerboard and ray designs. Functions are available for
graphical exploration of model goodness-of-fit and diagnostics, as well as
for synergy/antagonism assessment in 2D and 3D.
ggplot2, minpack.lm, BIGL (>= 0.9), rgl, nlme, Deriv, methods,
stats