Authors@R: c(person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", role = c("cre", "aut")),

Title: An extended Bayesian Learning Technique developed by Dr. Geoff
Description: AODE achieves highly accurate classification by averaging over all
Maintainer: Sai Teja Ranuva <ranuva.teja@gmail.com>
Depends: R (>= 2.14.0),discretization, functional,foreign,stringr
Packaged: 2013-07-25 07:14:31 UTC; ranuva.teja

Title: Analysis of Codon Data under Stationarity using a Bayesian
Authors@R: c(person("Cedric", "Landerer", role = c("aut", "cre"), email
= "cedric.landerer@gmail.com"), person("Gabriel", "Hanas", role
= "ctb"), person("Jeremy", "Rogers", role = "ctb"),
Author: Cedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers
Description: Is a collection of models to analyze genome scale codon
data using a Bayesian framework. Provides visualization
routines and checkpointing for model fittings. Currently
published models to analyze gene data for selection on codon
usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et
al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace
& Drummond (2013) <doi:10.1093/molbev/mst051>). In addition
'AnaCoDa' contains three currently unpublished models. The FONSE
(First order approximation On NonSense Error) model analyzes gene data
for selection on codon usage against of nonsense error rates.
The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use
ribosome footprinting data to analyze estimate ribosome pausing times with
and without nonsense error rate from ribosome footprinting data.

usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist
et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi
(Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In
addition 'AnaCoDa' contains three currently unpublished models.
The FONSE (First order approximation On NonSense Error) model
analyzes gene data for selection on codon usage against of
nonsense error rates. The PA (PAusing time) and PANSE (PAusing
time + NonSense Error) models use ribosome footprinting data to
analyze estimate ribosome pausing times with and without
nonsense error rate from ribosome footprinting data.