Description: Utilizes 11 peak quality metrics and 8 diverse machine learning algorithms to build a classifier for the automatic
Imports: reshape2, knitr, ggplot2, plotrix, tools, utils, klaR,
fastAdaboost, rpart, randomForest, kernlab, BiocStyle, methods,

Title: EDGE Taxonomy Assignments Visualization
Description: Implements routines for metagenome sample taxonomy assignments collection,
aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2,
BWA, Kraken, and MetaPhlAn. Produces SVG and PDF heatmap-like plots comparing taxa
Authors@R: person("Pavel", "Senin", , "psenin@lanl.gov", c("aut", "cre"))
Imports: reshape2, plyr, dplyr, data.table, ggplot2, Cairo

BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots
comparing taxa abundances across projects.
Authors@R: person("Pavel", "Senin", , "senin@hawaii.edu", c("aut", "cre"))

Title: Evaluate Periodicity in Large Scale Data
person("Gang", "Wu",, email = "wggucas@gmail.com", role = c("aut", "cre")),
person("Ron", "Anafi",, email = "ranafi@gmail.com", role = c("aut", "ctb")),
person("John", "Hogenesch",, email = "hogenesch@gmail.com", role = c("aut", "ctb")),
person("Michael", "Hughes",, email = "michael.evan.hughes@gmail.com", role = c("aut", "ctb")),
person("Karl", "Kornacker",, email = "kornacker@midohio.twcbc.com", role = c("aut", "ctb"))
Description: Provides two functions-meta2d and meta3d for detecting
rhythmic signals from time-series datasets. For analyzing
time-series datasets without individual information, 'meta2d' is
suggested, which could incorporates multiple methods from ARSER,
JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For
analyzing time-series datasets with individual information, 'meta3d' is
suggested, which takes use of any one of these three methods to analyze
time-series data individual by individual and gives out integrated values
based on analysis result of each individual.
Suggests: knitr, rmarkdown, diagram, shape

person("Karl", "Kornacker",, email = "kornacker@midohio.twcbc.com", role = c("aut", "ctb")),
person("Xavier", "Li",, email = "haohan.li.97@gmail.com", role = c("aut", "ctb")),
person("Matthew", "Carlucci",, email = "matt.carlucci.96@gmail.com", role = c("aut", "ctb"))
Description: There are two functions-meta2d and meta3d for
detecting rhythmic signals from time-series datasets. For analyzing