Provides a unified object-oriented framework for numerical 
    optimizers in R. Allows for both minimization and maximization with any 
    optimizer, optimization over more than one function argument, measuring of 
    computation time, setting a time limit for long optimization tasks.

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>.

A wrapper for 'optim' for nonlinear regression problems; see Nocedal J and Write S (2006, ISBN: 978-0387-30303-1).  Performs ordinary least squares (OLS), iterative re-weighted least squares (IRWLS), and maximum likelihood (MLE). Also includes the robust outlier detection (ROUT) algorithm; see Motulsky, H and Brown, R (2006) <doi:10.1186/1471-2105-7-123>.

Calculates autoecological data (optima and tolerance ranges) of a biological species
    given an environmental matrix. The package calculates by weighted averaging, using the number of occurrences to adjust the 
    tolerance assigned to each taxon to estimate optima and tolerance range in cases where taxa 
    have unequal occurrences. See the detailed methodology by Birks et al. (1990) <doi:10.1098/rstb.1990.0062>, and a 
    case example by Potapova and Charles (2003) <doi:10.1046/j.1365-2427.2003.01080.x>.

Provides a parallel version of the L-BFGS-B method of optim(). 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.