ugr
(Package: optextras) :
Wrapper for user gradient function for optimization tools
Provides a wrapper around user gradient function for nonlinear optimization to try to control for inadmissible arguments to user objective, gradient or hessian functions, as well as provide for maximization.
scalecheck
(Package: optextras) :
Check the scale of the initial parameters and bounds input to an optimization code
Nonlinear optimization problems often have different scale for different parameters. This function is intended to explore the differences in scale. It is, however, an imperfect and heuristic tool, and could be improved.
uhess
(Package: optextras) :
Wrapper for user Hessian function for optimization tools
Provides a wrapper around user analytic Hessian function for nonlinear optimization to try to control for inadmissible arguments to user that function, as well as provide for maximization.
gHgen
(Package: optextras) :
Generate gradient and Hessian for a function at given parameters.
gHgen is used to generate the gradient and Hessian of an objective function used for optimization. If a user-provided gradient function gr is available it is used to compute the gradient, otherwise package numDeriv is used. If a user-provided Hessian function hess is available, it is used to compute a Hessian. Otherwise, if gr is available, we use the function jacobian() from package numDeriv to compute the Hessian. In both these cases we check for symmetry of the Hessian. Computational Hessians are commonly NOT symmetric. If only the objective function fn is provided, then the Hessian is approximated with the function hessian from package numDeriv which guarantees a symmetric matrix.
Provides a replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, possibly box constrained functions of several or many parameters
ugHgenb
(Package: optextras) :
Generate gradient and Hessian for a function at given parameters
ugHgenb is used to generate the gradient and Hessian of an objective function used for optimization. If a user-provided gradient function gr is available it is used to compute the gradient via the wrapper ugr, otherwise package numDeriv is used. If a user-provided Hessian function hess is available, it is used to compute a Hessian via the wrapper uhess. However, we do not allow the user Hessian function to be specified if the user gradient function is NULL. If the user gr is available, we use the function jacobian() from package numDeriv to compute the Hessian. In both these cases we check for symmetry of the Hessian. Computational Hessians are commonly NOT symmetric. If only the objective function fn is provided, then the Hessian is approximated with the function hessian from package numDeriv which guarantees a symmetric matrix.
providing 
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