Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model
design attributes in the fit.  Design is a collection of about
180 functions that assist and streamline modeling, especially
for biostatistical and epidemiologic applications.  It also
contains new functions for binary and ordinal logistic
regression models and the Buckley-James multiple regression
model for right-censored responses, and implements penalized
maximum likelihood estimation for logistic and ordinary linear
models.  Design works with almost any regression model, but it
was especially written to work with logistic regression, Cox
regression, accelerated failure time models, ordinary linear
models, the Buckley-James model, and generalized least squares
for serially or spatially correlated observations.

c(person("Vinaykumar","L.N.", email="vinaymandya123@gmail.com", role = c( "aut","cre")), person("Vinayaka", email="vinayaka.b3vs@gmail.com" , role = c( "aut","ctb")),person("Dalal", "Ashutosh", email="ashutosh.dalal97@gmail.com" , role = c( "aut","ctb")))
Here, a function has been developed to generate parameters of the input designs, as well as incidence matrices. This is a general function that can be used to investigate the characterization properties of any block design.
Vinaykumar L.N. [aut, cre],
Vinayaka [aut, ctb],
Dalal Ashutosh [aut, ctb]

Design Clinical Trials with Potential Biomarker Effect
c(person("Yitao", "Lu", email = "yitaolu@uvic.ca",
role = c("aut","cre"),comment = c(ORCID = "0000-0002-0523-7416")),
person("Belaid", email = "bmoa@uvic.ca",
role = c("aut")), person("Julie","Zhou", email = "jzhou@uvic.ca",
role = c("aut")), person("Li","Xing", email = "li.xing@math.usask.ca",
role = c("aut"),comment = c(ORCID = "0000-0002-4186-7909")),
person("Xuekui","Zhang", email = "xuekui@uvic.ca",
role = c("aut"),comment = c(ORCID = "0000-0003-4728-2343")))
Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.
https://github.com/ubcxzhang/DesignCTPB, Y Lu (2020)
<doi:10.1002/sim.8868>
list( packages = list( list(package = "scipy", pip =
TRUE), list(package = "numba", pip = TRUE), list(package =
"pandas", pip = TRUE) ) )
NVIDIA CUDA GPU with compute capability 3.0 or
above and NVIDIA CUDA Toolkit 9.0 or above