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.
* The computation of the covariance matrix of the response/pseudodata
is very resource intensive if the random effects are in only a few groups.
When there are only few random effects an alternative computation would
be much better.
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.