An R Companion to Applied Regression, Third Edition, Sage (2019).

Title: Correlation-Adjusted Regression Survival (CARS) Scores
Description: Contains functions to estimate the Correlation-Adjusted Regression Survival (CARS) Scores. The method is described in Welchowski, T. and Zuber, V. and Schmid, M., (2018), Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection, <arXiv:1802.08178>.
Imports: Rcpp, survival, corpcor, mboost, fdrtool

email = "sorenh@math.aau.dk", role = c("aut", "cph"))
Description: Computer algebra via the 'SymPy' library (<https://www.sympy.org/>).
This makes it possible to solve equations symbolically,
find symbolic integrals, symbolic sums and other important quantities.

Suggests: Matrix, testthat (>= 2.1.0), knitr, rmarkdown

Title: Covariate-Adaptive Randomization for Clinical Trials
person(given="Wei", family="Ma", email="mawei@ruc.edu.cn", role=c("aut", "ths")),
Description: Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. It currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria.
Packaged: 2020-01-13 08:17:38 UTC; YeXiaoqing

Packaged: 2020-03-20 05:09:13 UTC; yexiaoqing

Description: Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria.