Package: crso
Title: Cancer Rule Set Optimization ('crso')
Version: 0.1.1
Author: Michael Klein <michael.klein@yale.edu>
Maintainer: Michael Klein <michael.klein@yale.edu>
Description: An algorithm for identifying candidate driver combinations in cancer. CRSO 
  is based on a theoretical model of cancer in which a cancer rule 
  is defined to be a collection of two or more events (i.e., alterations) that are minimally 
  sufficient to cause cancer. A cancer rule set is a set of cancer rules that collectively 
  are assumed to account for all of ways to cause cancer in the population. In CRSO every 
  event is designated explicitly as a passenger or driver within each patient. 
  Each event is associated with a patient-specific, event-specific passenger penalty, 
  reflecting how unlikely the event would have happened by chance, i.e., as a passenger.
  CRSO evaluates each rule set by assigning all samples to a rule in the rule set,
  or to the null rule, and then calculating the total statistical penalty from all
  unassigned event. CRSO uses a three phase procedure find the best rule set of 
  fixed size K for a range of Ks. A core rule set is then identified from among
  the best rule sets of size K as the rule set that best balances rule set size and 
  statistical penalty. 
  Users should consult the 'crso' vignette for an example walk through of a full CRSO run.
  The full description, of the CRSO algorithm is presented in: 
  Klein MI, Cannataro V, Townsend J, Stern DF and Zhao H. "Identifying combinations of cancer driver in individual patients." 
  BioRxiv 674234 [Preprint]. June 19, 2019. <doi:10.1101/674234>.
  Please cite this article if you use 'crso'.
Depends: R (>= 3.5.0), foreach
Imports: stats, utils
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-07-04 20:36:20 UTC; michaelklein
Repository: CRAN
Date/Publication: 2019-07-07 17:00:03 UTC
Built: R 4.3.3; ; 2025-01-24 15:24:33 UTC; unix
