R help - Sep 2011 - Nonlinear Regression

I'm wondering what packages exist to implement nonlinear least squares
regression in R other than 'nls'. Are there packages which implement
methods to estimate the optimum values of the parameters which do not
use the Gauss-Newton algorithm e.g. use Nelder Mead. In particular,
I'd be interested where this is done where the methods of the plinear
algorithm are also used (the initial step using the projection
matrix). If these don't exist why is the Gauss-Newton algorithm
considered sufficient?

Also can I implement the Levenberg Marquadt algorithm in nls?

Regards
Liam

optim()
optimx package
minpackLM package

and several others

JN

On 09/15/2011 06:00 AM, r-help-request at r-project.org
wrote:
> Message: 77
> Date: Wed, 14 Sep 2011 20:44:16 +0100
> From: Liam Brown <jackblake1930 at googlemail.com>
> To: r-help at r-project.org
> Subject: [R] Nonlinear Regression
> Message-ID:
> 	<CA+MHRQ=L4vnWAgCFKC=a+0R0r740tnsW1qu2BowPrU9CZ9TwmQ at
mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
> 
> I'm wondering what packages exist to implement nonlinear least squares
> regression in R other than 'nls'. Are there packages which
implement
> methods to estimate the optimum values of the parameters which do not
> use the Gauss-Newton algorithm e.g. use Nelder Mead. In particular,
> I'd be interested where this is done where the methods of the plinear
> algorithm are also used (the initial step using the projection
> matrix). If these don't exist why is the Gauss-Newton algorithm
> considered sufficient?
> 
> Also can I implement the Levenberg Marquadt algorithm in nls?
> 
> Regards
> Liam
>