nimOptim

Initial values for the parameters to be optimized over.

A function to be minimized (or maximized), with first argument the
vector of parameters over which minimization is to take place. It
should return a scalar result.

A function to return the gradient for the "BFGS", "CG" and "L-BFGS-B" methods.

The method to be used. See `Details` section of <code><a rd-options="" href="/link/optim?package=nimble&version=0.6-6" data-mini-rdoc="nimble::optim">optim</a></code>. One of:
"Nelder-Mead", "BFGS", "CG", "L-BFGS-B".
Note that the R methods "SANN", "Brent" are not supported.

method

Vector or scalar of lower bounds for parameters.

lower

Vector or scalar of upper bounds for parameters.

upper

A list of control parameters. See <code>Details</code> section of <code><a rd-options="" href="/link/optim?package=nimble&version=0.6-6" data-mini-rdoc="nimble::optim">optim</a></code>.

control

Logical. Should a Hessian matrix be returned?

hessian

Nimble wrapper around R's builtin <code><a rd-options="" href="/link/optim?package=nimble&version=0.6-6" data-mini-rdoc="nimble::optim">optim</a></code>.

A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <http://r-nimble.org>.

Christopher Paciorek

nimble

MCMC, Particle Filtering, and Programmable Hierarchical Modeling

nimOptim function

The method to be used. See `Details` section of <code><a rd-options='' href='optim'>optim</a></code>. One of:
"Nelder-Mead", "BFGS", "CG", "L-BFGS-B".
Note that the R methods "SANN", "Brent" are not supported.

A list of control parameters. See <code>Details</code> section of <code><a rd-options='' href='optim'>optim</a></code>.

Nimble wrapper around R's builtin <code><a rd-options='' href='optim'>optim</a></code>.

Nimble wrapper around R's builtin <code><a rd-options='' href='optim'>optim</a></code>. — nimOptim

Nimble wrapper around R's builtin <code><a rd-options='' href='optim'>optim</a></code>.

Nimble wrapper around R's builtin <code><a rd-options="" href="/link/optim?package=nimble&version=0.6-6" data-mini-rdoc="nimble::optim">optim</a></code>.

nimOptim: Nimble wrapper around R's builtin `optim`.

Description

Usage

Arguments

Value

See Also