c(person("Devin", "Francom", role = c("aut", "cre"), email = "devinfrancom@gmail.com"),
person("Bruno", "Sanso", role = "ths"))
knitr, testthat (>= 2.1.0), parallel, MASS, wmtsa, formatR
Devin Francom [aut, cre],
Bruno Sanso [ths]

Bayesian fitting and sensitivity analysis methods for adaptive
spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as
functional or scalar output. An extension of the methodology in Denison, Mallick
and Smith (1998) <doi:10.1023/A:1008824606259>.
knitr, testthat (>= 2.1.0), parallel, MASS, formatR

Classification of mRNA and lncRNA Sequences using Complex
Network Theory
The methodology applied in this package starts with the creation of networks from RNA sequences, in this way the extraction of resources from these networks is performed with a threshold methodology, aiming to make a classification between the classes of the sequences. There are two data present in the package, "sequences" and "sequences2" with 11 and 10 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>.
igraph, Biostrings, RWeka, randomForest, rgl, rmcfs,
grDevices, graphics, stats

Classification of RNA Sequences using Complex Network Theory
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "sequences" and "sequences2" with 11 and 10 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>. These sequences are used to run examples.
igraph, Biostrings, RWeka, randomForest, rmcfs, grDevices,
graphics, stats, rJava

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.