runCNORode: runCNORode
In CNORode: ODE add-on to CellNOptR

A one-line wrapper of the CNORode pipeline

runCNORode(model, data, compression = TRUE,
  results_folder = "CNORode_results", cutNONC = TRUE,
  expansion = FALSE, LB_n = 1, LB_k = 0.1, LB_tau = 0.01,
  UB_n = 5, UB_k = 0.9, UB_tau = 10, default_n = 3,
  default_k = 0.5, default_tau = 1, opt_n = TRUE, opt_k = TRUE,
  opt_tau = TRUE, random = TRUE, maxeval = 1e+05, maxtime = 60,
  transfer_function = 3, nan_fac = 1, lambda_tau = 0, lambda_k = 0)

`model`	A filename of prior knowledge network (PKN) in the SIF format
`data`	A measurement filename in the MIDAS format
`compression`	compress the prior knowledge network (TRUE), see `preprocessing`
`results_folder`	results folder for the analysis.
`cutNONC`	cut non-observable non-controllable node from PKN (TRUE), see `preprocessing`
`expansion`	expand OR gates in the PKN (FALSE), see `preprocessing`
`LB_n`	lower bound on parameter n, see `createLBodeContPars`
`LB_k`	lower bound on parameter k, see `createLBodeContPars`
`LB_tau`	lower bound on parameter tau, see `createLBodeContPars`
`UB_n`	upper bound on parameter n, see `createLBodeContPars`
`UB_k`	upper bound on parameter k, see `createLBodeContPars`
`UB_tau`	upper bound on parameter tau, see `createLBodeContPars`
`default_n`	default value of parameter n, see `createLBodeContPars`
`default_k`	default value of parameter k, see `createLBodeContPars`
`default_tau`	default value of parameter tau, see `createLBodeContPars`
`opt_n`	should parameter n be optimised, see `createLBodeContPars`
`opt_k`	should parameter k be optimised, see `createLBodeContPars`
`opt_tau`	should parameter tau be optimised, see `createLBodeContPars`
`random`	initial parameter vector generation (TRUE: random, FALSE: half of the LB-UB)
`maxeval`	maximum number of funciton evaluations in the optimisation, see `parEstimationLBodeSSm`
`maxtime`	maximum CPU time (in seconds) spent on optimisation before calling final refinement, see `parEstimationLBodeSSm`
`transfer_function`	trandfer function types represented by the edges, see `parEstimationLBodeSSm`
`nan_fac`	penalty for NA simulations, see `parEstimationLBodeSSm`
`lambda_tau`	regularisation penalty for tau parameters, see `parEstimationLBodeSSm`
`lambda_k`	regularisation penalty for k parameters for optimisation, see `parEstimationLBodeSSm`

## Not run: 
model = system.file("extdata", "ToyModelMMB_FeedbackAnd.sif",package="CNORode")
data = system.file("extdata", "ToyModelMMB_FeedbackAnd.csv", package="CNORode")
res = runCNORode(model,data,results_folder = "./results")

## End(Not run)