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DESCRIPTION
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Package: AnaCoDa
Type: Package
Title: Analysis of Codon Data under Stationarity using a Bayesian
Framework
Version: 0.1.4.0
PreviousGitCommitHash: ad2611c3611ec51486d0654ec56c7a5ff25a416c
The above is the hash for the previous commit
Date: 2021-12-18
Author: c(person("Cedric", "Landerer", role = c("aut", "cre"), email
= "[email protected]"), person("Gabriel", "Hanas", role
= "ctb"), person("Jeremy", "Rogers", role = "ctb"),
person("Alex", "Cope", role="aut", "ctb"), person("Denizhan", "Pak", role="ctb",
person("Michael", "Gilchrist", role="ctb", email = "[email protected]"))
Maintainer: Alex Cope <[email protected]>
URL: https://github.com/clandere/AnaCoDa
VignetteBuilder: knitr
NeedsCompilation: yes
Depends: R (>= 3.3.0), Rcpp (>= 0.11.3), VGAM, methods, mvtnorm
Suggests: knitr, Hmisc, coda, testthat, lmodel2
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addition 'AnaCoDa' contains three currently unpublished models.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: 7.1.1