Package: RobinCar 1.1.0

RobinCar: Robust Inference for Covariate Adjustment in Randomized Clinical Trials

Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized clinical trials. Ting Ye, Jun Shao, Yanyao Yi, Qinyuan Zhao (2023) <doi:10.1080/01621459.2022.2049278>. Ting Ye, Marlena Bannick, Yanyao Yi, Jun Shao (2023) <doi:10.1080/24754269.2023.2205802>. Ting Ye, Jun Shao, Yanyao Yi (2023) <doi:10.1093/biomet/asad045>. Marlena Bannick, Jun Shao, Jingyi Liu, Yu Du, Yanyao Yi, Ting Ye (2024) <doi:10.1093/biomet/asaf029>. Xiaoyu Qiu, Yuhan Qian, Jaehwan Yi, Jinqiu Wang, Yu Du, Yanyao Yi, Ting Ye (2025) <doi:10.48550/arXiv.2408.12541>.

Authors:Marlena Bannick [cre, aut], Yuhan Qian [aut], Ting Ye [aut], Yanyao Yi [aut], Faith Bian [aut]

RobinCar_1.1.0.tar.gz
RobinCar_1.1.0.zip(r-4.7)RobinCar_1.1.0.zip(r-4.6)RobinCar_1.1.0.zip(r-4.5)
RobinCar_1.1.0.tgz(r-4.6-any)RobinCar_1.1.0.tgz(r-4.5-any)
RobinCar_1.1.0.tar.gz(r-4.7-any)RobinCar_1.1.0.tar.gz(r-4.6-any)
RobinCar_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
RobinCar/json (API)
NEWS

# Install 'RobinCar' in R:

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

Bug tracker:https://github.com/mbannick/robincar/issues

On CRAN:

5.15 score 7 stars 19 scripts 596 downloads 17 exports 133 dependencies

Last updated from:6727754898. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	219
source / vignettes	OK	242
linux-release-x86_64	OK	205
macos-release-arm64	OK	212
macos-oldrel-arm64	OK	186
windows-devel	OK	156
windows-release	OK	174
windows-oldrel	OK	166
wasm-release	OK	167

Exports:%>%car_pb car_ps car_sr data_gen data_gen2 robincar_calibrate robincar_contrast robincar_covhr robincar_coxscore robincar_glm robincar_linear robincar_logrank robincar_mh robincar_SL robincar_SL_median robincar_tte

Dependencies:AIPW askpass backports base64enc bit bit64 bitops blob broom bslib cachem callr caTools cellranger cli clipr codetools conflicted cpp11 crayon curl cvAUC data.table DBI dbplyr digest dplyr dtplyr emulator evaluate farver fastDummies fastmap fontawesome forcats foreach fs future future.apply gam gargle generics ggplot2 globals glue googledrive googlesheets4 gplots gtable gtools haven highr hms htmltools httr ids isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lifecycle listenv lubridate magrittr MASS Matrix memoise mime modelr mvtnorm nnls numDeriv openssl parallelly pillar pkgconfig prettyunits processx progress progressr ps purrr R6 ragg rappdirs rbibutils RColorBrewer Rcpp RcppArmadillo Rdpack readr readxl rematch rematch2 reprex rlang rmarkdown ROCR Rsolnp rstudioapi rvest S7 sass scales selectr stringi stringr SuperLearner survival sys systemfonts textshaping tibble tidyr tidyselect tidyverse timechange tinytex truncnorm tzdb utf8 uuid vctrs viridisLite vroom withr xfun xml2 yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generate permuted block treatment assignments	car_pb
Generate Pocock-Simon minimization treatment assignments	car_ps
Generate simple randomization treatment assignments	car_sr
Data generation function from JRSS-B paper	data_gen
Data generation function from covariate adjusted log-rank paper	data_gen2
Print calibration result	print.CalibrationResult
Print contrast result	print.ContrastResult
Print glm model result	print.GLMModelResult
Print linear model result	print.LinModelResult
Print MH result	print.MHResult
Print TTE result	print.TTEResult
Perform linear or joint calibration	robincar_calibrate
Estimate a treatment contrast	robincar_contrast
Covariate-adjusted estimators for time to event data	robincar_covhr
Robust cox score adjustment	robincar_coxscore
Covariate adjustment using generalized linear working model	robincar_glm
Covariate adjustment using linear working model	robincar_linear
Robust (potentially stratified) logrank adjustment	robincar_logrank
Estimate Mantel-Haenszel Risk Difference	robincar_mh
BETA: Covariate adjustment using working models from the super learner libraries through the AIPW package with cross-fitting.	robincar_SL
BETA: Covariate adjustment using working models from the super learner libraries through the AIPW package with cross-fitting, with median adjustment.	robincar_SL_median
Covariate adjustment for time to event data	robincar_tte