Package: AnaCoDa
Type: Package
Title: Analysis of Codon Data under Stationarity using a Bayesian
        Framework
Version: 0.1.0
Date: 2018-01-10
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"),
        person("Alex", "Cope", role="ctb"))
Author: Cedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers
        [ctb], Alex Cope [ctb]
Maintainer: Cedric Landerer <cedric.landerer@gmail.com>
URL: https://github.com/clandere/RibModelFramework
NeedsCompilation: yes
Depends: R (>= 3.3.0), Rcpp (>= 0.11.3), methods
Suggests: Hmisc, VGAM, coda, testthat
RcppModules: Test_mod, Trace_mod, CovarianceMatrix_mod,
        MCMCAlgorithm_mod, Model_mod, Parameter_mod, Genome_mod,
        Gene_mod, SequenceSummary_mod
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        'AnaCoDa' contains three currently unpublished models.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.
License: GPL (>= 2)
Imports:
LinkingTo: Rcpp
LazyLoad: yes
LazyData: yes
RoxygenNote: 6.0.1
Packaged: 2018-01-10 18:27:01 UTC; clandere
Repository: CRAN
Date/Publication: 2018-01-11 12:01:10 UTC
