c(
person(given = "Kai",
family = "Husmann",
role = c("aut", "cre"),
email = "kai.husmann@uni-goettingen.de",
comment = c(ORCID = "0000-0003-2970-4709")),
person(given = "Volker",
family = "von Groß",
role = "aut",
email = "volker.vongross@uni-goettingen.de",
comment = c(ORCID = "0000-0001-7372-0066")),
person(given = "Jasper",
family = "Fuchs",
role = "aut",
email = "jasper.fuchs@uni-goettingen.de",
comment = c(ORCID = "0000-0001-5951-7897")),
person(given = "Kai",
family = "Bödeker",
role = "aut",
email = "kai.boedeker@tum.de",
comment = c(ORCID = "0000-0002-5307-5108")),
person("Goettingen University - Forest Economics and Sustainable Land-use Planning",
role=c("cph", "fnd")),
person("TUM School of Life Sciences - Forest Management",
role=c("cph", "fnd"))
)
Kai Husmann [aut, cre] (<https://orcid.org/0000-0003-2970-4709>),
Volker von Groß [aut] (<https://orcid.org/0000-0001-7372-0066>),
Jasper Fuchs [aut] (<https://orcid.org/0000-0001-5951-7897>),
Kai Bödeker [aut] (<https://orcid.org/0000-0002-5307-5108>),
Goettingen University - Forest Economics and Sustainable Land-use
Planning [cph, fnd],
TUM School of Life Sciences - Forest Management [cph, fnd]

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>.

Parallel Versions of the Gradient-Based optim() Methods
Provides parallel versions of the gradient-based optim() methods. The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
R.rsp, roxygen2, spam, microbenchmark, testthat, ggplot2,
numDeriv