jac_mat

internal

Performs robust estimation and inference when using covariate adjustment
and/or covariate-adaptive randomization in randomized controlled trials.
This package is trimmed to reduce the dependencies and validated to be used across industry.
See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>,
Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>,
Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>,
Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>,
and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.

Liming Li

RobinCar2

ROBust INference for Covariate Adjustment in Randomized Clinical
Trials

Marlena Bannick

Dong Xi

Ting Ye

Yanyao Yi

Gregory Chen

Gilead Sciences, Inc. 

F. Hoffmann-La Roche AG 

Merck Sharp & Dohme, Inc. 

AstraZeneca plc 

Eli Lilly and Company 

The University of Washington 

jac_mat function

Obtain the Jacobian matrix — jac_mat

Obtain the Jacobian matrix

jac_mat: Obtain the Jacobian matrix

Description

Usage

Arguments