ISSUES:

* gamm4 isn't handling NA's properly with formulae like y~factor(z).
  When inserting z into dataframe, it fails to drop.

* gamm4 (and indeed gamm) will fail if the fixed effects are not identifiable. 
  This can happen, quite easily. e.g.  s(x,by=fac1) + s(x,by=fac2) means that
  the columns x:fac1 and x:fac2 are not independent.

0.3-0

* Pass optimizer arguments to lme4 / lmer

* Speed up random effect design matrix creation with a sparse non-zero 
  indexing approach

* Disable condVar when calling ranef

* Add the option to use scikit-sparse to compute the Cholesky decomposition 
  of the data covariance matrix.

* Use Woodbury identity to speed up cov matrix computation when random effect
  cov matrix has more rows than columns (currently without python option).

* Fix of nested random effect example in helpfile where effects were not 
  nested! 

0.2-7

* Matrix::chol no longer (since 1.6-3) returns the pivot sequence, rendering 
  it not very useful for many purposes and breaking the covariance matrix 
  computation in gamm4 - fixed by this version. Thanks to Balint Tamasi for
  reporting the covariance matrix problem. 

0.2-6

* Removal of code branch compatible with lme<1.0 to avoid warnings.

0.2-5

* Fix of problem dealing with long random effect formulae. Thanks to 
  Iain Malcolm.

* Added control ability to pass control arguments to underlying lme4 fitting
  functions.

0.2-4

* start up message changed to use packageStartupMessage.

* Various imports in NAMESPACE.

0.2-3

* modified to extract REML using REMLcrit to avoid lme4 warning

* argument drop.unused.levels added to gamm4, to facilitate mrf smoothing when
  some regions are data-less. 

0.2-2

* Some tidying and modification to avoid cran check problems for code
  back compatible with old lme4.

* Fix of erroroneous passing of '...' to modular lme4 fitting functions.

0.2-1

* Some restructuring - initial model setup in common for both old and new 
  lme4, but actual fitting work then branches depending on version. 
	
0.2-0

* Updated for new lme4. gamm4 tests for lme4 version installed, and behaves 
  accordingly. Will generate notes on testing with old lme4 - unavoidable. Some 
  testing still to do - awaiting some lme4 fixes. 

* fix to variance computation - prior weights were not used correctly - could
  cause incorrect interval estimation for smooths when prior weights used
  (e.g. binomial with n > 1, or weighted gaussian case).

0.1-6

* added return of R factor from QR of WX.

* cov matrix computation modified to use LAPACK and pivoting properly.

* some changes to replace direct slot access with getME, but incomplete (y, pWt
  and var still direct).

0.1-5

* gamm4 return object now of class "gamm4"

0.1-4

* upgrade to gamm4.setup to make smooth to r.e. conversion object oriented
  and hence cleaner.

* gamm4 can now deal with "sf" class smooth factor interactions. This gives 
  an efficient way to handle subject specific random smooths.

0.1-3

* Incorrect pivoting of covariance matrix of data/pseudodata could lead 
  to incorrect covariance matrix for coefficients and incorrect EDF 
  computation. Pivoting now corrected, and results tested. 

0.1-2

* Bad bug fix: I'd failed to track an internal lme4 change, so that 
  gamm4 had stopped extracting random effect variances correctly. 
  This meant that gamm4 standard errors were typically too low. 
  Fixed and checks added to test suite to detect this sort of problem.  

0.1-1

* Allow for centering of smooth model matrix columns, when there is an 
  intercept, but columns are not centered by constraint.

0.1-0

* Upgraded to use `t2' type tensor product smooths

* bug fix so that s(...,fx=TRUE) works

* workaround in gamm4 so that g/lmer handles offset properly.

0.0-4

* covariance matrix calculation was still not robust enough. Improved 
further.

0.0-3

* solving for the coefficient covariance matrix could fail under heavy 
  smoothing --- now made more robust. 

* `gamm4' can now be supplied with prior weights. 

* The `cbind(success,failure)' form for a binomial response now works 
  properly.

* help file has been updated for mgcv_1.6-2, and to avoid running to 
  many slow `gamm' calls in checking.

0.0-2

* gamm4 now returns a `scale.estimated' field in its `gam' object
  part. 
