Imports: parallel, grid, graphics, grDevices, stats, utils

Suggests: XML, RCurl, lattice, ncdf4, igraph, bigmemory, knitr,

Authors@R: c(person("Barry", "Grant", role=c("aut", "cre"),
Imports: Rcpp, parallel, grid, graphics, grDevices, stats, utils
sequence and dynamics data. Features include the ability to read and write
structure, sequence and dynamic trajectory data, perform sequence and structure
database searches, data summaries, atom selection, alignment, superposition,
rigid core identification, clustering, torsion analysis, distance matrix
analysis, structure and sequence conservation analysis, normal mode analysis,
principal component analysis of heterogeneous structure data, and correlation
network analysis from normal mode and molecular dynamics data. In addition,
various utility functions are provided to enable the statistical and graphical
power of the R environment to work with biological sequence and structural data.
Please refer to the URLs below for more information.

testthat (>= 0.9.1), httr, msa, Biostrings

rmarkdown, testthat (>= 0.9.1), httr, msa, Biostrings

Title: BioConductor Package Downloads Stats
Description: Download stats reports from the BioConductor.org stats website.

Description: Download stats reported from the BioConductor.org stats website.

Title: Sensitivity Analysis and Optimum Experiment Design for Microbial
person("Jose Lucas", "Penalver", email = "", role = "aut"),
person("Jose A.", "Egea", email = "josea.egea@upct.es", role = "aut")
Description: Extends the bioinactivation package with functions for Sensitivity
Imports: FME (>= 1.3.2), bioinactivation (>= 1.1.2), corrplot(>= 0.73),
Suggests: knitr (>= 1.9), testthat (>= 0.9.1), rmarkdown

person("Jose Lucas", "Penalver-Soto", email = "", role = "aut"),
Suggests: knitr (>= 1.9), testthat (>= 0.9.1), rmarkdown (>= 1.12)

Title: Simulation of deterministic and stochastic biochemical reaction
bioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions
For deterministic solutions, bioPN creates the associated system of differential equations "on the fly", and
solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
For stochastic solutions, bioPN offers variants of Gillespie algorithm, or SSA.
it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
bioPN algorithms are developed in C to achieve adequate performance.