via bootstrap and (for G-estimation) recursive sandwich estimation. The authors
thank Gabrielle Simoneau of McGill University for identifying (and fixing) some
errors in an earlier version of the code.

Author: Michael Wallace, Erica E M Moodie, David A Stephens and Gabrielle Simoneau

Title: DTR Estimation and Inference via G-Estimation, Dynamic WOLS,
Q-Learning, and Dynamic Weighted Survival Modeling (DWSurv)
via bootstrap and (for G-estimation) recursive sandwich estimation. Estimation
and inference for survival outcomes via Dynamic Weighted Survival Modeling

Packaged: 2019-10-03 14:56:03 UTC; Gabrielle

Author: Michael Wallace, Erica E M Moodie, David A Stephens, Gabrielle Simoneau and Juliana Schulz
Description: Dynamic treatment regime estimation and inference via G-estimation, dynamic weighted ordinary least squares (dWOLS) and Q-learning. Inference via bootstrap and (for G-estimation) recursive sandwich estimation. Estimation and inference for survival outcomes via Dynamic Weighted Survival Modeling (DWSurv). Extension to continuous treatment variables (gdwols). Wallace et al. (2017) <DOI:10.18637/jss.v080.i02>; Simoneau et al. (2020) <DOI:10.1080/00949655.2020.1793341>.
Imports: graphics, stats, utils, dplyr, MASS

Title: Drug Target Set Enrichment Analysis
person(given = "Yinchun", family = "Su", role  = "aut"))
Description: It is a novel tool used to identify the candidate drugs against a particular disease based on a drug target set enrichment analysis. It assumes the most effective drugs are those with a closer affinity in the protein-protein interaction network to the specified disease. (See Gómez-Carballa et al. (2022) <doi: 10.1016/j.envres.2022.112890> and Feng et al. (2022) <doi: 10.7150/ijms.67815> for disease expression profiles; see Wishart et al. (2018) <doi: 10.1093/nar/gkx1037> and Gaulton et al. (2017) <doi: 10.1093/nar/gkw1074> for drug target information; see Kanehisa et al. (2021) <doi: 10.1093/nar/gkaa970> for the details of KEGG database.)
Imports: BiocParallel, dplyr, fgsea, igraph, magrittr, tibble, tidyr
Suggests: future, future.apply, knitr, rmarkdown