R help - Jan 2007 - New version of lme4 and new mailing list R-SIG-mixed-models

Version 0.9975-11 of the lme4 package has been uploaded to CRAN.  The
source package should be available on the mirrors in a day or two and
binary packages should follow soon after.

There are several changes in this release of the package.  The most
important is the availability of a development version of lmer called,
for the time being, lmer2.  At present lmer2 only fits linear mixed
models.  Generalized linear mixed models will be added "soon".
Furthermore there is no mcmcsamp method for a model fit by lmer2.
This deficiency will also be rectified "soon".  Once I have all the
capabilities and methods currently available for lmer also available
for the new representation I will remove the old representation and
rename lmer2 as lmer.

The current version of lmer will continue to be available throughout
the migration process.  You don't have to change anything about your
use of that function unless you want to try the new one.  It would be
a good idea, however, to save the data and the call to lmer in
addition to saving an lmer object, if you so choose, so that you can
recreate the fitted model when the development version becomes the
release version.

The package contains a vignette giving the details of the new implementation.

The reason I am releasing a development version in parallel with the
production version is because I would like feedback from useR's
regarding the development version.  In my experience, testing it
myself and with colleagues whom I visited recently, I have found that
lmer2 is faster and more reliable than the current lmer.  In
particular, on some difficult model fits I have been able to get
substantially better parameter estimates (i.e. the deviance at the
lmer2 estimates is perhaps 4 or 5 lower than that at the lmer
estimates) with lmer2 than I could with lmer.

If you have fit a linear mixed model using lmer and are willing to try
it with lmer2 I would appreciate your telling me if the parameter
estimates are comparable and which fit was faster (use system.time()
to check).  I'm primarily interested in models fit to large data sets
or "difficult" fits.

We have established a new mailing list, R-SIG-mixed-models, for
discussion of R software to fit mixed-effects models, especially lmer.
 See https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models for
information or to subscribe.

I know that I have said this before but this is the last time that I
am going to change the underlying representation.  Really - trust me -
this is the last time.  My theory of software development is expressed
in a line from an old blues song, "you just keep doing it wrong till
you do it right".  I'm convinced that this time I have it right.  That
statement sounds like "famous last words", doesn't it?  :-)

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On Thu, 2007-01-25 at 17:12 -0600, Douglas Bates wrote: 
> Version 0.9975-11 of the lme4 package has been uploaded to CRAN.  The
> source package should be available on the mirrors in a day or two and
> binary packages should follow soon after.
> 
> There are several changes in this release of the package.  The most
> important is the availability of a development version of lmer called,
> for the time being, lmer2.  At present lmer2 only fits linear mixed
> models.  Generalized linear mixed models will be added "soon".
> Furthermore there is no mcmcsamp method for a model fit by lmer2.
> This deficiency will also be rectified "soon".  Once I have all
the
> capabilities and methods currently available for lmer also available
> for the new representation I will remove the old representation and
> rename lmer2 as lmer.
> 
> The current version of lmer will continue to be available throughout
> the migration process.  You don't have to change anything about your
> use of that function unless you want to try the new one.  It would be
> a good idea, however, to save the data and the call to lmer in
> addition to saving an lmer object, if you so choose, so that you can
> recreate the fitted model when the development version becomes the
> release version.
> 
> The package contains a vignette giving the details of the new
implementation.
> 
> The reason I am releasing a development version in parallel with the
> production version is because I would like feedback from useR's
> regarding the development version.  In my experience, testing it
> myself and with colleagues whom I visited recently, I have found that
> lmer2 is faster and more reliable than the current lmer.  In
> particular, on some difficult model fits I have been able to get
> substantially better parameter estimates (i.e. the deviance at the
> lmer2 estimates is perhaps 4 or 5 lower than that at the lmer
> estimates) with lmer2 than I could with lmer.
> 
> If you have fit a linear mixed model using lmer and are willing to try
> it with lmer2 I would appreciate your telling me if the parameter
> estimates are comparable and which fit was faster (use system.time()
> to check).  I'm primarily interested in models fit to large data sets
> or "difficult" fits.
> 
> We have established a new mailing list, R-SIG-mixed-models, for
> discussion of R software to fit mixed-effects models, especially lmer.
>  See https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models for
> information or to subscribe.
> 
> I know that I have said this before but this is the last time that I
> am going to change the underlying representation.  Really - trust me -
> this is the last time.  My theory of software development is expressed
> in a line from an old blues song, "you just keep doing it wrong till
> you do it right".  I'm convinced that this time I have it right. 
That
> statement sounds like "famous last words", doesn't it?  :-)
Doug,

Your efforts are most appreciated.

To the points you raise in the last paragraph, I might note the
following quote:

"In developing a methodology, leave room for improvement. 
 It is absolutely critical not to kill a field by doing too
 good a job on the first outing." - 

          "How to be a Highly Cited Author in the Mathematical
Sciences",
           Dr. David Donoho, Stanford University, 2002
           http://www.in-cites.com/scientists/DrDavidDonoho.html

:-)

Best regards,

Marc Schwartz