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	*	   Version 0.999375-16 NEWS		 *
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		 CHANGES IN lme4 VERSION 0.999375-16

This midsummer release has many, many changes, relative to earlier
versions.  Be careful.

SIGNIFICANT USER-VISIBLE CHANGES

    o   The underlying algorithms and representations for all the
        mixed-effects models fit by this package have changed - for
        the better, we hope.  The class "mer" is a common
        mixed-effects model representation for linear, generalized
        linear, nonlinear and generalized nonlinear mixed-effects
        models.

    o   ECME iterations are no longer used at all, nor are analytic
        gradients.  Components named 'niterEM', 'EMverbose', or
        'gradient' can be included in the 'control' argument to
        lmer(), glmer() or nlmer() but have no effect.

    o   PQL iterations are no longer used in glmer() and nlmer().
        Only the Laplace approximation is currently available.  AGQ,
        for certain classes of GLMMs or NLMMs, is being added.

    o   The 'method' argument to lmer(), glmer() or nlmer() is
        deprecated.  Use the 'REML = FALSE' in lmer() to obtain ML
        estimates.  Selection of AGQ in glmer() and nlmer() will be
        controlled by the argument 'nAGQ', when completed.

NEW FEATURES

    o   The representation of mixed-effects models has been
        dramatically changed to allow for smooth evaluation of the
        objective as the variance-covariance matrices for the random
        effects approach singularity.  Beta testers found this
        representation to be more robust and usually faster than
        previous versions of lme4.

    o   The mcmcsamp function uses a new sampling method for the
        variance-covariance parameters the allows recovery from
        singularity.  The update is not based on a sample from the
        Wishart distribution.  It uses a redundant parameter
        representation and a linear least squares update.

	CAUTION: Currently the results from mcmcsamp look peculiar and
	are probably incorrect.  I hope it is just a matter of my
	omitting a scaling factor but I have seen patterns such as
	the parameter estimate for some variance-covariance parameters
	being the maximum value in the chain, which is highly
	unlikely.

    o   The 'verbose' argument to lmer(), glmer() and nlmer() can be
        used instead of 'control = list(msVerbose = TRUE)'.