Statistical Analysis of Amplicon Data of the Same Sample to
Identify Artefacts

Anja Lange [aut, cre], Daniel Hoffmann [aut]

c(person("Michal", "Burdukiewicz", email =
"michalburdukiewicz@gmail.com", role = c("cre", "aut")),
person("Piotr", "Sobczyk", email = "sobbombo@gmail.com", role = "ctb"),
person("Stefan", "Roediger", email = "stefan.roedigier@b-tu.de", role = "ctb"))
Predicts amyloid proteins using random forests trained on the
n-gram encoded peptides. The implemented algorithm can be accessed from
both the command line and shiny-based GUI.
Michal Burdukiewicz [cre, aut],
Piotr Sobczyk [ctb],
Stefan Roediger [ctb]

c(person("Michal", "Burdukiewicz", email = "michalburdukiewicz@gmail.com", role = c("cre", "aut")),
person("Piotr", "Sobczyk", email = "sobbombo@gmail.com", role = "ctb"),
person("Stefan", "Roediger", email = "stefan.roedigier@b-tu.de", role = "ctb"))

An extended Bayesian Learning Technique developed by Dr. Geoff
Webb
AODE achieves highly accurate classification by averaging over all
of a small space.

Analysis of Codon Data under Stationarity using a Bayesian
Framework
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"))
Cedric Landerer [aut, cre], Gabriel Hanas [ctb], Jeremy Rogers
[ctb], Alex Cope [ctb]
Test_mod, Trace_mod, CovarianceMatrix_mod,
MCMCAlgorithm_mod, Model_mod, Parameter_mod, Genome_mod,
Gene_mod, SequenceSummary_mod
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'AnaCoDa' contains three currently unpublished models.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.