R (>= 2.8.0), futile.paradigm (>= 1.1.2), futile.options

R (>= 2.8.0), futile.paradigm (>= 1.2.0), futile.options

R (>= 2.8.0), futile.paradigm (>= 2.0.0), futile.options

Provides a simple yet powerful logging utility. Based loosely on
log4j, futile.logger takes advantage of R idioms to make logging a
convenient and easy to use replacement for cat and print statements.
'options.R' 'appender.R' 'constants.R' 'layout.R' 'logger.R'
'scat.R' 'futile.logger-package.R'

utils, lambda.r (>= 1.1.0), futile.options

A collection of matrix manipulation functions

R (>= 2.8.0), futile.paradigm (>= 2.0.0), futile.logger (>=
1.2.0), futile.any (>= 1.2.0), RMTstat, RUnit
A collection of matrix manipulation functions including
generation of matrix ensembles

futile.logger (>= 1.3.0), futile.any (>= 1.3.0), RMTstat

Random matrix generation and manipulation
lambda.r (>= 1.1.6), lambda.tools, futile.logger (>= 1.3.0),
RMTstat
A collection of functions for manipulating matrices and generating ensembles of random matrices

Random Matrix Generation and Manipulation
utils, lambda.r (>= 1.1.6), lambda.tools, futile.logger (>=
1.3.0), RMTstat
A collection of functions for manipulating matrices and generating
ensembles of random matrices. Used primarily to identify the cutoff point
for the noise portion of the eigenvalue spectrum.

A scoped options management framework. Used in other packages.

A framework for working in a functional programming paradigm in
R
Provides dispatching implementations suitable for
functional programming paradigms. The framework provides a
mechanism for attaching guards to functions similar to Erlang,
while also providing the safety of assertions reminiscent of
Eiffel.

Interim Analysis of Operational Futility in Randomized Trials
with Time-to-Event Endpoints and Fixed Follow-Up
Yingying Zhuang and Michal Juraska, with contributions from Doug Grove, Peter B. Gilbert, and Alexander R. Luedtke
Randomized clinical trials commonly follow participants for a time-to-event efficacy endpoint for a fixed period of time. Consequently, at the time when the last enrolled participant completes their follow-up, the number of observed endpoints is a random variable. Assuming data collected through an interim timepoint, simulation-based estimation and inferential procedures in the standard right-censored failure time analysis framework are conducted for the distribution of the number of endpoints--in total as well as by treatment arm--at the end of the follow-up period. The future (i.e., yet unobserved) enrollment, endpoint, and dropout times are generated according to mechanisms specified in the simTrial() function in the 'seqDesign' package. A Bayesian model for the endpoint rate is used for generating future data (see the vignette for details).