Identify the optimal timing for new treatment initiation during multiple state disease transition, including  multistate model fitting, simulation of mean residual lifetime for a given transition state, and estimation of confidence interval. The method is referred to de Wreede, L., Fiocco, M., & Putter, H. (2011) <doi:10.18637/jss.v038.i07>.

Computes confidence regions on the location of response surface optima. Response surface models can be up to cubic polynomial models in up to 5 controllable factors, or Thin Plate Spline models in 2 controllable factors.

Provides a set of commands to manage an abstract
        optimization method. The goal is to provide a building block
        for a large class of specialized optimization methods. This
        package manages: the number of variables, the minimum and
        maximum bounds, the number of non linear inequality
        constraints, the cost function, the logging system, various
        termination criteria, etc...

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.

Provides a derivative-based optimization framework that allows users 
    to combine eight convergence criteria. Unlike standard optimization functions, this package includes a 
    built-in mechanism to verify the positive definiteness of the Hessian matrix 
    at the point of convergence. This additional check helps prevent the solver 
    from falsely identifying non-optimal solutions, such as saddle points, as 
    valid minima.

Provides general purpose tools for helping users to implement steepest
             gradient descent methods for function optimization; for details see
             Ruder (2016) <arXiv:1609.04747v2>. Currently, the Steepest 2-Groups
             Gradient Descent and the Adaptive Moment Estimation (Adam) are the
             methods implemented. Other methods will be implemented in the future.