| optimx-package | A replacement and extension of the optim() function, plus various optimization tools |
| as.data.frame.optimx | General-purpose optimization |
| 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. |
| checksolver | Test if requested solver is present |
| coef.opm | Summarize opm object |
| coef.optimx | Summarize opm object |
| coef<- | Summarize opm object |
| coef<-.opm | Summarize opm object |
| coef<-.optimx | Summarize opm object |
| ctrldefault | set control defaults |
| dispdefault | set control defaults |
| 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 |
| hjn | Compact R Implementation of Hooke and Jeeves Pattern Search Optimization |
| kktchk | Check Kuhn Karush Tucker conditions for a supposed function minimum |
| multistart | General-purpose optimization - multiple starts |
| opm | General-purpose optimization |
| optchk | General-purpose optimization |
| optimr | General-purpose optimization |
| optimx | General-purpose optimization |
| optsp | Forward difference numerical gradient approximation. |
| polyopt | General-purpose optimization - sequential application of methods |
| proptimr | Compact display of an 'optimr()' result object |
| Rcgmin | An R implementation of a nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. |
| Rcgminb | An R implementation of a bounded nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA 'Rcgmin' AND DO NOT USE DIRECTLY. |
| Rcgminu | An R implementation of an unconstrained nonlinear conjugate gradient algorithm with the Dai / Yuan update and restart. Based on Nash (1979) Algorithm 22 for its main structure. CALL THIS VIA 'Rcgmin' AND DO NOT USE DIRECTLY. |
| Rvmmin | Variable metric nonlinear function minimization, driver. |
| Rvmminb | Variable metric nonlinear function minimization with bounds constraints |
| Rvmminu | Variable metric nonlinear function minimization, unconstrained |
| scalechk | Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization |
| snewton | Safeguarded Newton methods for function minimization using R functions. |
| snewtonm | Safeguarded Newton methods for function minimization using R functions. |
| summary.optimx | Summarize optimx object |
| tn | Truncated Newton minimization of an unconstrained function. |
| tnbc | Truncated Newton function minimization with bounds constraints |
| [.optimx | General-purpose optimization |