bSMED

This package implements the adaptive sampling algorithm from the paper "Batch Sequential Minimum Energy Design with Design-Region Adaptation" by Heeyoung Kim et al. (2017), published in Journal of Quality Technology Vol. 49, No. 1, January 2017.

I have used the abbreviation bSMED to refer to this method. The main function provided by this package is the bSMED function, which creates an R6 object that performs the algorithm.

Installation

You can install bSMED from github with:

# install.packages("devtools")
devtools::install_github("CollinErickson/bSMED")

Example

See the vignette for a more in-depth description of the following example.

This is a basic example which shows you how to solve a common problem:

## basic example code

# Get function
quad_peaks_slant <- TestFunctions::add_linear_terms(function(XX) {.2+.015*TestFunctions::add_zoom(TestFunctions::rastrigin, scale_low = c(.4,.4), scale_high = c(.6,.6))(XX)^.9}, coeffs = c(.02,.01))

# Create bSMED instance
a <- bSMED::bSMED$new(D=2,func=quad_peaks_slant,
                      obj="func", b=3, nb=5,
                      X0=lhs::maximinLHS(20,2),
                      Xopts=lhs::maximinLHS(500,2),
                      package="GauPro",
                      parallel=FALSE
                      )
a$run()

#> Best design point is
#>       0.262 0.725 
#>  with objective value
#>       0.628346 
#> Best predicted point over domain is 
#>       0.257 0.744 
#>  with objective value
#>       0.6317774