| 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. | 
| checkallsolvers | Test if requested solver is present | 
| 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. | 
| grpracma | A reorganization of the call to the pracma 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 | 
| ncg | An R implementation of a Dai / Yuan nonlinear conjugate gradient algorithm. | 
| nvm | Variable metric nonlinear function minimization, driver. | 
| opm | General-purpose optimization | 
| opm2optimr | Extract optim() solution for one method of opm() result | 
| 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 Dai / Yuan nonlinear conjugate gradient algorithm. | 
| Rcgminb | An R implementation of a Dai / Yuan nonlinear conjugate gradient algorithm. | 
| Rcgminu | An R implementation of a Dai / Yuan nonlinear conjugate gradient algorithm. | 
| Rtnmin | Truncated Newton function minimization | 
| Rvmmin | Variable metric nonlinear function minimization, driver. | 
| Rvmminb | Variable metric nonlinear function minimization, driver. | 
| Rvmminu | Variable metric nonlinear function minimization, driver. | 
| scalechk | Check the scale of the initial parameters and bounds input to an optimization code used in nonlinear optimization | 
| snewtm | Safeguarded Newton methods for function minimization using R functions. | 
| snewton | Safeguarded Newton methods for function minimization using R functions. | 
| summary.optimx | Summarize optimx object | 
| tn | Truncated Newton function minimization | 
| tnbc | Truncated Newton function minimization | 
| [.optimx | General-purpose optimization |