Tools to Support Optimization Possibly with Bounds and Masks

Documentation for package ‘optextras’ version 2016-8.8

axsearch	Perform axial search around a supposed minimum and provide diagnostics
bmchk	Check bounds and masks for parameter constraints used in nonlinear optimization
bmstep	Compute the maximum step along a search direction.
fnchk	Run tests, where possible, on user objective function
gHgen	Generate gradient and Hessian for a function at given parameters.
gHgenb	Generate gradient and Hessian for a function at given parameters.
grback	Backward difference numerical gradient approximation.
grcentral	Central difference numerical gradient approximation.
grchk	Run tests, where possible, on user objective function and (optionally) gradient and hessian
grfwd	Forward difference numerical gradient approximation.
grnd	A reorganization of the call to numDeriv grad() function.
hesschk	Run tests, where possible, on user objective function and (optionally) gradient and hessian
kktchk	Check Kuhn Karush Tucker conditions for a supposed function minimum
optextras	A replacement and extension of the optim() function, plus various optimization tools
optsp	Forward difference numerical gradient approximation.
scalechk	Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization