Expanded Replacement and Extension of the 'optim' Function

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Documentation for package ‘optimx’ version 2023-10.21

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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 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
ncg 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.
ncgqs 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.
nvm Variable metric nonlinear function minimization, driver.
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
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 minimization of an unconstrained function.
tnbc Truncated Newton function minimization with bounds constraints
[.optimx General-purpose optimization