1.3-19

* numerous changes to NAMESPACE and gamm related functions to pass
  codetools checks.

* gam.method()  modified to allow GACV as an option for outer GCV 
  model selection.

* Improved outer iteration added via gdi.c coupled with gam.fit3. 
  Exact first and second derivatives of GACV/GCV/UBRE/AIC are now 
  available via new iteration methods. These improve the 
  speed and reliability of fitting in the generalized case. 

* magic.c::mgcv_mmult modified so that all inner loop calculations are 
  optimal (i.e. inner loop pointers increments are all 1).

* `smooth.construct' functions for "cc" and "cr" smooths now increase `k'
  to the minimum possible value (and warn), if it's too low. 

* `gam' modified to allow passing of `mustart' etc to gam.fit and 
  gam.fit2, properly

* `gam' modified to fix a bug whereby fitting in two steps using argument 
  `G' could fail when some sp's are to be estimated and some fixed.

* an argument `in.out' added to `gam' to allow user initialization of 
  smoothing parameters when using `outer' iteration in the generalized 
  case. This can speed up analyses that rely on several refits of the same 
  model. 

1.3-18

* gamm modifed so that weights dealt with properly if lme type varFunc 
  used. This is only possible in the non-generalized case, as gamm.Rd 
  now clarifies.

* slight modification to s() to add `width.cutoff=500' to `deparse'

* by variables not handled properly in p-spline example in 
  smooth.construct.Rd - fixed.

* bug fix in summary.gam.Rd example (pmax -> pmin)

* color example added to plot.gam.Rd

* bug fix in `smooth.construct.tensor.smooth.spec' - class "cyclic.smooth"
  marginals no longer re-parameterized.

* `te' documentation modified to mention that marginal reparameterization 
  can destabilize tensor products. 

1.3-17

* print.summary.gam prints estimated ranks more prettily (thanks Martin 
  Maechler)

* `fix.family.link' can now handle the `cauchit' link, and also appends a
   third derivative of link function to the family (not yet used).

* `fix.family.var' now adds a second derivative of the link function to 
   the family (not yet used).

* `magic' modified to (i) accept an argument `rss.extra' which is added to 
  the  RSS(squared norm) term in the GCV/UBRE or scale calculation; (ii)
  accept argument `n.score' (defaults to number of data), the number to 
  use in place of the number of data in the GCV/UBRE calculation.
  These are useful for dealing with very large data sets using 
  pseudo-model approaches.

* `trans' and `shift' arguments added to `plot.gam': allows, e.g. single
   smooth models to be easily plotted on uncentred response scale.

* Some .Rd bug fixes. 

* Addition of choose.k.Rd helpfile, including example code for diagnosing 
  overly restrictive choice of smoothing basis dimension `k'.

1.3-16

* bug fix in predict.gam documentation + example of how to predict from a 
  `gam' outside `R'.

1.3-15

* chol(A,pivot=TRUE) now (R 2.3.0) generates a warning if `A' is not +ve 
  definite. `mroot' modified to supress this (since it only calls 
  `chol(A,pivot=TRUE)' because `A' is usually +ve semi-definite). 

1.3-14

* mat.c:mgcv_symeig modified to allow selection of the LAPACK routine
  actually used: dsyevd is the routine used previously, and seems very 
  reliable. dsyevr is the faster, smaller more modern version, which it
  seems possible to break... rest of code still calls dsyevd.

* Symbol registration added (thanks largely to Brian Ripley). Version 
  depends on R >= 2.3.0

1.3-13

* some doc changes

* The p-values for smooth terms had too low power sometimes. Modified 
  testing  procedure so that testing rank is at most 
  ceiling(2*edf.for.term). This gives quite close to uniform p-value 
  distributions when the null is true, in simulations, without excessive 
  inflation of the p-values, relative to parametetric equivalents when 
  it is not. Still not really satisfactory.

1.3-12

* vis.gam could fail if the original model formula contained functions of 
  covariates, since vis.gam calls predict.gam with a newdata argument 
  based on the *model frame* of the model object. predict.gam now 
  recognises that this has happened and doesn't fail if newdata is a model 
  frame which contains, e.g. log(x) rather than x itself. offset handling 
  simplified as a result.

* prediction from te smooths could fail becuase of a bug in handling the 
  list of re-parameterization matrices for 1-D terms in 
  Predict.matrix.tensor.smooth. Fixed. (tensor product docs also updated)

* gamm did not handle s(...,fx=TRUE) terms properly, due to several 
  failures to count s(...,fx=FALSE) terms properly if there were fixed 
  terms present. Now fixed.

* In the gaussian additive mixed model case `gamm' now allows "ML" or 
  "REML" to be selected (and is slightly more self consistent in 
  handling the results of the two alternatives).

1.3-11

* added package doc file

* added French error message support (thanks to Philippe Grosjean), and 
error message quotation characters (thanks to Brian Ripley.)

1.3-10

* a `constant' attribute has been added to the object returned by
  predict.gam(...,type="terms"), although what is returned is still not an 
  exact match to what `predict.lm' would do. 

* na.action handling made closer to glm/lm functions. In particular,
  default for predict.gam is now to pad predictions with NA's as opposed
  to dropping rows of newdata containing NA's. 

* interpret.gam had a bug caused by a glitch in the terms.object 
  documentation (R <=2.2.0). Formulae such as y ~ a + b:a + s(x) could 
  cause failure. This was because attr(tf,"specials") is documented as 
  returning indices of specials in `terms'. It doesn't, it indexes 
  specials in the variables dimension of the attr(tf,"factors") table: 
  latter now used to translate.

* `by' variable use could fail unreasonably if a `by' variable was not of 
  mode `numeric': now coerced to numeric at appropriate times in smooth
  constructors. 

1.3-9

* constants multiplying TPRS basis functions were `unconventional' for d 
  odd in function eta() in tprs.c. The constants are immaterial if you are 
  using gam, gamm etc, but matter if you are trying to get out the 
  explicit representation of a TPRS term yourself (e.g. to differentiate 
  a smooth exactly).

1.3-8

* get.var() now checks that result is numeric or factor (avoids 
  occasional problems with variable names that are functions - e.g `t')

* fix.family.var and fix.family.link now pass through unaltered any family 
  already containing the extra derivative functions. Usually, to make a 
  family work with gam.fit2 it is only necessary to add a dvar function.

* defaults modified so that when using outer iteration, several performance
  iteration steps are now used for initialization of smoothing parameters 
  etc. The number is controlled by gam.control(outerPIsteps). This tends
  to lead to better starting values, especially with binary data. gam, 
  gam.fit and gam.control are modified.

* initial.sp modified to allow a more expensive intialization method, but
  this is not currently used by gam.

* minor documentation changes (e.g. removal of full stops from titles)

1.3-7

* change to `pcls' example to account for model matrix rescaling changing 
smoothing parameter sizes.

* `gamm' `control' argument set to use "L-BFGS-B" method if `lme' is using 
`optim' (only does this if `nlminb' not present). Consequently `mgcv' now 
depends on nlme_3.1-64 or above.

* improvement of the algorithm in `initial.sp'. Previously it was possible 
for very low rank smoothers (e.g. k=3) to cause the initialization to 
fail, because of poor handling of unpenalized parameters. 

1.3-6

* pdIdnot class changed so that parameters are variances not standard 
deviations - this makes for greater consistency with pdTens class, and 
means that limits on notLog2 parameterization should mean the same thing 
for both classes. 

* niterEM set to 0 in lme calls. This is because EM steps in lme are not
 set up to deal properly with user defined pdMat classes (latter 
 confirmed by DB).

1.3-5

* Improvements to anova and summary functions by Henric Nilsson 
  incorporated. Functions are now closer to glm equivalents, and 
  printing is more informative. See ?anova.gam and ?summary.gam.

* nlme 3.1-62 changed the optimizer underlying lme, so that indefintie 
  likelihoods cause problems. See ?logExp2 for the workaround.
  - niterEM now reset to 25, since parameterization prevents parameters 
  wandering to +/- infinity (this is important as starting values for 
  Newton steps are now more critical, since reparameterization 
  introduces new local minima).

* smoothCon modified to rescale penalty coefficient matrices to have 
  similar `size' to X'X for each term. This is to try and ensure that 
  gamm is reasonably scale invariant in its behaviour, given the 
  logExp2 re-parameterization.

* magic dropped dimensions of an array inapproporiately - fixed.

* gam now checks that model does not have more coefficients than data.

1.3-4

* inst/CITATION file added. Some .Rd fixes

30/6/2005 1.3-3

* te() smooths were not always estimated correctly by gamm(): invariance 
  lost and different results to equivalent s() smooths. The problem seems
  to lie in a sensitivity of lme() estimation to the absolute size of the 
  `S' attribute matrices of a pdTens class pdMat object: the problem did 
  not occur at the last revision of the pdTens class, and there are no 
  changes logged for nlme that could have caused it, so I guess it's down
  to a change in something that lme calls in the base distribution. 
  To avoid the problem, smooth.construct.tensor.smooth.spec has been 
  modified to scale all marginal penalty matrices so that they have 
  largest singular value 1.

* Changes to GLMs in R 2.1.1 mean that if the response is an array, gam 
  could fail, due to failure of terms like w * X when w is and array 
  rather than a vector. Code modified accordingly.

* Outer iteration now suppresses some warnings, until the final fitted
  model is obtained, in order to avoid printing warnings that actually
  don't apply to the final fit.

* Version number reporting made (hopefully) more robust.

* pdconstruct.pdTens removed absolute lower limit on coef - replaced with
  relative lower limit.

* moved tensor product constraint construction to BEFORE by variable
  stuff in smooth.construct.tensor.smooth.spec.


