'gestalt.R' 'utils.R' 'compose.R' 'constant.R' 'partial.R'
'fn.R' 'context.R' 'posure.R'

'gestalt.R' 'utils.R' 'closure.R' 'compose.R' 'constant.R'
'partial.R' 'fn.R' 'context.R' 'posure.R'

Provides a suite of function-building tools centered around a
(forward) composition operator, %>>>%, which extends the semantics of the
'magrittr' %>% operator and supports 'Tidyverse' quasiquotation. It enables
you to construct composite functions that can be inspected and transformed as
list-like objects. In conjunction with %>>>%, a compact function constructor,
fn(), and a partial-application constructor, partial(), are also provided;
both support quasiquotation.
magrittr (>= 1.5), testthat (>= 3.0.0), knitr, rmarkdown

Generalised Survival Trial Assessment Tool Environment
c(
person("James", "Bell", email = "james.bell.ext@boehringer-ingelheim.com", role = c("aut", "cre")),
person("Jasmin", "Ruehl", email = "",role = "ctb"))
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing calculation of these same properties. Finally, it also provides parametric event prediction using blinded trial data, including creation of confidence intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment curves.
foreach, doParallel, shiny, shinythemes, survRM2, survival,
methods
James Bell [aut, cre],
Jasmin Ruehl [ctb]

Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
foreach, doParallel, shiny, shinythemes, survival, methods