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

Title: Random Matrix Generation and Manipulation
Imports: utils, lambda.r (>= 1.1.6), lambda.tools, futile.logger (>=
Description: 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.

Description: A scoped options management framework
Packaged: Mon Apr 5 21:09:07 2010; brian

Description: A scoped options management framework. Used in other packages.

Title: A framework for working in a functional programming paradigm in
Description: 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
Packaged: Mon Aug 9 19:49:55 2010; brian

Packaged: Tue Aug 17 20:46:38 2010; brian

Packaged: Thu Aug 19 20:07:15 2010; brian

Packaged: Mon Aug 23 19:46:33 2010; brian

Packaged: Thu Sep 9 00:45:18 2010; brian

Packaged: Sun Sep 12 20:15:24 2010; brian

Packaged: Mon Sep 13 19:00:26 2010; brian

Packaged: Mon Sep 27 19:55:23 2010; brian

Packaged: Mon Oct 4 22:10:45 2010; brian

Packaged: Sun Oct 17 22:45:52 2010; root

Packaged: Wed Oct 20 20:38:31 2010; root

Title: Interim Analysis of Operational Futility in Randomized Trials
with Time-to-Event Endpoints and Fixed Follow-Up
Author: Yingying Zhuang and Michal Juraska, with contributions from Doug Grove, Peter B. Gilbert, and Alexander R. Luedtke
Description: 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).