Title: Standard Benchmark Optimization Functions
Description: A set of standard benchmark optimization functions for R and
Imports: lhs, randtoolbox, stats, stringr

Title: Graphical and Numerical Checks for Mode-Finding Routines
Description: Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function.  This is accomplished graphically by calculating all one-dimensional "projection plots" of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed.  The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.
Suggests: testthat, quantreg, mclust, knitr, rmarkdown

Description: Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1007/s10457-020-00519-0>.

person("Goettingen University - Forest Economics and Sustainable Land-use Planning",
person("TUM School of Life Sciences - Forest Management",
Author: Kai Husmann [aut, cre] (<https://orcid.org/0000-0003-2970-4709>),
Goettingen University - Forest Economics and Sustainable Land-use
TUM School of Life Sciences - Forest Management [cph, fnd]

Description: Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.