Rvmminb  R Documentation 
A boundsconstarined R implementation of a variable metric method for minimization of nonlinear functions subject to bounds (box) constraints and masks (fixed parameters).
See manual Rvmmin.Rd for more details and examples.
Rvmminb(par, fn, gr, lower, upper, bdmsk, control = list(), ...)
par 
A numeric vector of starting estimates. 
fn 
A function that returns the value of the objective at the
supplied set of parameters 
gr 
A function that returns the gradient of the objective at the
supplied set of parameters Note that a gradient function MUST be provided. See the manual for

lower 
A vector of lower bounds on the parameters. 
upper 
A vector of upper bounds on the parameters. 
bdmsk 
An indicator vector, having 1 for each parameter that is "free" or unconstrained, and 0 for any parameter that is fixed or MASKED for the duration of the optimization. 
control 
An optional list of control settings. See the manual Rvmmin.Rd for details. 
... 
Further arguments to be passed to 
This routine is intended to be called from Rvmmin
, which will, if
necessary, supply a gradient approximation. However, some users will want
to avoid the extra overhead, in which case it is important to provide an
appropriate and highaccuracy gradient routine.
Note that bounds checking, if it is carried out, is done by Rvmmin
.
Functions fn
must return a numeric value.
A list with components:
par 
The best set of parameters found. 
value 
The value of the objective at the best set of parameters found. 
counts 
A vector of two integers giving the number of function and gradient evaluations. 
convergence 
An integer indicating the situation on termination of the function.

message 
A description of the situation on termination of the function. 
bdmsk 
Returned index describing the status of bounds and masks at the proposed solution. Parameters for which bdmsk are 1 are unconstrained or "free", those with bdmsk 0 are masked i.e., fixed. For historical reasons, we indicate a parameter is at a lower bound using 3 or upper bound using 1. 
optim
## See Rvmmin.Rd
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