Package: AnaCoDa
Type: Package
Title: Analysis of Codon Data under Stationarity using a Bayesian
        Framework
Version: 0.1.4.4
Date: 2020-09-11
Author: Authors@R
Maintainer: Cedric Landerer <cedric.landerer@gmail.com>
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, markdown
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        addition 'AnaCoDa' contains three currently unpublished models.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
Packaged: 2020-09-15 09:04:26 UTC; landerer
Repository: CRAN
Date/Publication: 2020-09-15 09:40:19 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2025-11-01 02:14:59 UTC; windows
Archs: x64
