Implements likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance.

Estimate the non-linear odds ratio and plot it against a continuous exposure.

Setup, run and analyze 'NetLogo' (<https://www.netlogo.org>) model simulations in 'R'.
    'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. 
    However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses.
    The user defines all information that is needed in an intuitive framework, using class objects.
    Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user.
    By nesting model calls in future environments, large simulation design with many runs can be executed in parallel.
    This also enables simulating 'NetLogo' experiments on remote high performance computing machines.
    In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.

Adds brute force and multiple starting values to nls.

Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.

Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).

We solve non linear least squares problems with optional
    equality and/or inequality constraints. Non linear iterations are
    globalized with back-tracking method. Linear problems are solved by
    dense QR decomposition from 'LAPACK' which can limit the size of
    treated problems. On the other side, we avoid condition number
    degradation which happens in classical quadratic programming approach.
    Inequality constraints treatment on each non
    linear iteration is based on 'NNLS' method (by Lawson and Hanson).
    We provide an original function 'lsi_ln' for solving linear least squares
    problem with inequality constraints in least norm sens. Thus if Jacobian of
    the problem is rank deficient a solution still can be provided.
    However, truncation errors are probable in this case.
    Equality constraints are treated by using a basis of Null-space.
    User defined function calculating residuals must return a list having
    residual vector (not their squared sum) and Jacobian. If Jacobian is
    not in the returned list, package 'numDeriv' is used to calculated
    finite difference version of Jacobian. The 'NLSIC' method was fist
    published in Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.