Package: tmle3mopttx 1.0.0

Ivana Malenica

tmle3mopttx: Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment

This package estimates the optimal individualized treatment rule for the categorical treatment using Super Learner (sl3). In order to avoid nested cross-validation, it uses split-specific estimates of Q and g to estimate the rule as described by Coyle et al. In addition, it provides the Targeted Maximum Likelihood estimates of the mean performance using CV-TMLE under such estimated rules. This is an adapter package for use with the tmle3 framework and the tlverse software ecosystem for Targeted Learning.

Authors:Ivana Malenica [aut, cre], Jeremy Coyle [aut, cph], Mark van der Laan [aut, ths], Haodong Li [ctb]

tmle3mopttx_1.0.0.tar.gz
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tmle3mopttx_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
tmle3mopttx/json (API)

# Install 'tmle3mopttx' in R:

install.packages('tmle3mopttx', repos = c('https://tlverse.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tlverse/tmle3mopttx/issues

Datasets:

data_bin - Mock data set with Binary Treatment
data_cat - Mock data set with Categorical Treatment
data_cat_realistic - Mock data set with Categorical Treatment and rare treatment
data_cat_vim - Mock data set for Variable Importance Analysis with Categorical Treatment

On CRAN:

categorical-treatment causal-inference heterogeneous-effects machine-learning optimal-individualized-treatment targeted-learning variable-importance

4.89 score 13 stars 1 packages 201 scripts 14 exports 125 dependencies

Last updated from:3c0e8437af. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	266
source / vignettes	OK	232
linux-release-x86_64	ERROR	264
macos-release-arm64	ERROR	160
macos-oldrel-arm64	ERROR	239
windows-devel	ERROR	208
windows-release	ERROR	209
windows-oldrel	ERROR	197
wasm-release	OK	239

Exports:create_mv_learners LF_rule normalize_rows Optimal_Rule_Q_learning Optimal_Rule_Revere Param_TSM_name Q_learning tmle3_mopttx_blip_revere tmle3_mopttx_Q tmle3_mopttx_vim tmle3_Spec_mopttx_blip_revere tmle3_Spec_mopttx_Q tmle3_Spec_mopttx_vim vals_from_factor

Dependencies:abind assertthat backports base64enc BBmisc bitops bslib cachem caret caTools checkmate class cli clock codetools cpp11 crayon data.table delayed diagram digest dplyr e1071 evaluate farver fastmap fontawesome foreach fs future future.apply generics ggplot2 glmnet globals glue gower gplots gtable gtools hal9001 hardhat highr hms htmltools htmlwidgets igraph ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix memoise mime ModelMetrics mvtnorm nlme nnet numDeriv origami parallelly pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr R.methodsS3 R.oo R.utils R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack recipes reshape2 rlang rmarkdown ROCR rpart rstackdeque S7 sass scales shape sl3 sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tinytex tmle3 tzdb utf8 uuid vctrs viridisLite visNetwork withr xfun yaml

Help page	Topics
Make SL of multivariate learners	create_mv_learners
Mock data set with Binary Treatment	data_bin
Mock data set with Categorical Treatment	data_cat
Mock data set with Categorical Treatment and rare treatment	data_cat_realistic
Mock data set for Variable Importance Analysis with Categorical Treatment	data_cat_vim
Dynamic Likelihood Factor	LF_rule
Normalize rows	normalize_rows
Learns the Optimal Rule given a tmle_task and likelihood, using Q learning.	Optimal_Rule_Q_learning
Learning the Optimal Rule using the Revere framework	Optimal_Rule_Revere
Treatment Specific Mean with names specifying the covariates the rule depends on.	Param_TSM_name
Q learning wrapper	Q_learning
Mean under the Optimal Individualized Treatment Rule	tmle3_mopttx_blip_revere
Mean under the Optimal Individualized Treatment Rule, using Q learning	tmle3_mopttx_Q
Mean under the Optimal Individualized Treatment Rule	tmle3_mopttx_vim
TMLE for the Mean Under the Optimal Individualized Rule	tmle3_Spec_mopttx_blip_revere
Defines the Mean Under the Optimal Individualized Rule with Categorical Treatment, estimated using Q learning (single step)	tmle3_Spec_mopttx_Q
Variable Importance with the Mean Under the Optimal Individualized Rule	tmle3_Spec_mopttx_vim
Get factors	vals_from_factor

Package: tmle3mopttx 1.0.0

tmle3mopttx: Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment

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