You can install the SigOptR package directly from CRAN, or you can use the devtools package to grab the latest version from GitHub.

CRAN

Install the SigOptR package directly from CRAN:

install.packages("SigOptR", repos = "http://cran.us.r-project.org")
library(SigOptR)

Latest Version from GitHub

Install the latest SigOptR package from GitHub by using devtools:

install.packages("devtools", repos = "http://cran.us.r-project.org")
library(devtools)
install_github("sigopt/SigOptR")
library(SigOptR)

Now, you can run SigOpt's Optimization Loop.

First, setup the experiment:

install.packages("devtools", repos = "http://cran.us.r-project.org")
library(devtools)
install_github("sigopt/SigOptR")
library(SigOptR)
Sys.setenv(SIGOPT_API_TOKEN=SIGOPT_API_TOKEN)


experiment <- create_experiment(list(
  name="Franke Optimization (R)",
  # Define which parameters you would like to tune
  parameters=list(
    list(name="x", type="double", bounds=list(min=0.0, max=1.0)),
    list(name="y", type="double", bounds=list(min=0.0, max=1.0))
  ),
  metrics=list(
  	list(name="function_value")
  ),
  parallel_bandwidth=1,
  # Define an Observation Budget for your experiment
  observation_budget=30,
  project="sigopt-examples"
))
print(paste(
  "Created experiment: https://app.sigopt.com/experiment",
  experiment$id,
  sep="/"
))

Then, run the optimization loop itself:

# Evaluate your model with the suggested parameter assignments
# Franke function - http://www.sfu.ca/~ssurjano/franke2d.html
evaluate_model <- function(assignments) {
  return(franke(assignments$x, assignments$y))
}

# Run the Optimization Loop until the Observation Budget is exhausted
while(experiment$progress$observation_count < experiment$observation_budget) {
  suggestion <- create_suggestion(experiment$id)
  value <- evaluate_model(suggestion$assignments)
  create_observation(experiment$id, list(
    suggestion=suggestion$id,
    value=value
  ))

  # Update experiment object
  experiment <- fetch_experiment(experiment$id)
}