cppOptimize: Interface to the internal C++ optimization routine "optimize"
In glmBfp: Bayesian Fractional Polynomials for GLMs
Description
Usage
Arguments
Value
Author(s)
Description
Interface to the internal C++ optimization routine "optimize"
Usage
1
cppOptimize(f_, min.x, max.x, prec = .Machine$double.eps^0.25)
Arguments
f_
the function
min.x
minimum bound on x
max.x
maximum bound on x
prec
precision (same default as for the original optimize function)
Value
A list with the following elements:
- par
the minimum abscissa found by the algorithm
- inv.hessian
the inverse Hessian at par
- evaluations
list of the function evaluation pairs: args and
vals
Author(s)
Daniel Sabanes Bove daniel.sabanesbove@ifspm.uzh.ch
glmBfp documentation built on July 2, 2020, 2:30 a.m.
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Description Usage Arguments Value Author(s)
Description
Interface to the internal C++ optimization routine "optimize"
Usage
1 | cppOptimize(f_, min.x, max.x, prec = .Machine$double.eps^0.25)
|
Arguments
f_ |
the function |
min.x |
minimum bound on x |
max.x |
maximum bound on x |
prec |
precision (same default as for the original optimize function) |
Value
A list with the following elements:
- par
the minimum abscissa found by the algorithm
- inv.hessian
the inverse Hessian at
par- evaluations
list of the function evaluation pairs:
argsandvals
Author(s)
Daniel Sabanes Bove daniel.sabanesbove@ifspm.uzh.ch
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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