Parse options from the command-line using a simple, clean syntax.
It requires little or no specification and supports short and long options,
GNU-, Java- or Microsoft- style syntaxes, verb commands and more.
testthat (>= 0.9.1), knitr (>= 1.9), rmarkdown

c(
person("Christopher", "Brown", , "chris.brown@decisionpatterns.com", c("aut", "cre") ),
person("Daryl", "McCullough", role="aut"),
person("Decision Patterns", role = "cph")
)
Christopher Brown [aut, cre],
Daryl McCullough [aut],
Decision Patterns [cph]

person("Thomas", "Torsney-Weir", email="torsneyt@gmail.com",
role=c("aut", "cre"))
A set of standard benchmark optimization functions for R and
a common interface to sample them.
'optim.functions.R' 'vectorize.R' 'sampling.R'
'single.objective.R' 'functions.R'

Graphical and Numerical Checks for Mode-Finding Routines
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.
testthat, quantreg, mclust, knitr, rmarkdown

c(
person(given = "Kai",
family = "Husmann",
role = c("aut", "cre"),
email = "kai.husmann@uni-goettingen.de"),
person(given = "Volker",
family = "von Groß",
role = "aut",
email = "volker.vongross@uni-goettingen.de"),
person(given = "Jasper",
family = "Fuchs",
role = "aut",
email = "jasper.fuchs@uni-goettingen.de"),
person(given = "Kai",
family = "Bödeker",
role = "aut",
email = "kai.boedeker@tum.de")
)
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>.
Kai Husmann [aut, cre],
Volker von Groß [aut],
Jasper Fuchs [aut],
Kai Bödeker [aut]