Flexible optimizer with numerous input specifications for detailed
  parameterisation. Designed for complex loss functions with state and 
  parameter space constraints. Visualization tools for validation and analysis
  of the convergence are included.

Provides a unified object-oriented framework for numerical
    optimizers in R. Supports minimization and maximization with any optimizer,
    optimization over more than one function argument, computation time
    measurement, and time limits for long optimization tasks.

A collection of numerical optimization algorithms. 
    One is a simple implementation of
    the primitive grid search algorithm, the other is an extension of the
    simulated annealing algorithm that can take custom boundaries into
    account. The methodology for this bounded simulated annealing
    algorithm is due to Haario and Saksman (1991), <doi:10.2307/1427681>.

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>.