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**  denotes quite substantial/important changes
*** denotes really big changes 

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Currently deprecated and liable to be removed:
- gam performance iteration (1.8-19, Sept 2017)

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* Fix of bug whereby testing for OpenMP and nthreads>1 in bam, would fail if 
  OpenMP was missing. 


* When functions were added to families within mgcv some very large 
  environments could end up attached to those functions, for no good reason.
  The problem originated from the dispatch of the generic '' 
  and then propagated via and This is now avoided,
  resulting in smaller gam objects on disk and lower R memory usage. 
  Thanks to Niels Richard Hansen for uncovering this. 

* Another bug fix for prediction from discrete fit bam models with an offset, 
  this time when there were more than 50000 data. Also fix to bam fitting when 
  the number of data was an integer multiple of the chunk size + 1. 

* check.term was missing a 'stop' so that some unhandled nesting structures
  in bam(...,discrete=TRUE) failed with an unhelpful error, instead of a 
  helpful one. Fixed.

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* bam(,discrete=TRUE) could produce garbage with ti(x,z,k=c(6,5),mc=c(T,F))
  because tensor re-ordering for efficiency failed to re-order mc (this is 
  a *very* specialist bug!). Thanks to Fabian Scheipl.  

* plot(...,residuals=TRUE) weighted the working residuals by the sqrt working 
  weights divided by the mean sqrt working weight. The standardization by the 
  mean sqrt weight was non standard and has been removed.

* Fix to bad bug in bam(...,discrete=TRUE) offset handling, and predict.bamd 
  modified to avoid failure predicting with offset. Thanks to Paul Shearer.

* fix of typo in, which caused failure of extended families
  when dataset larger than chunk size. Thanks Martijn Wieling.

* bam(...,discrete=TRUE)/bgam.fitd modified to use fisher weights with 
  extended families if rho!=0.

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** bam() now accepts extended families (i.e. nb, tw, ocat etc)

* now allows stratification (i.e. baseline hazard can differ between 

* Example code for matched case control in ? was just plain wrong. Now
  fixed. Thanks to Patrick Farrell.  

* bam(...,discrete=TRUE) tolerance for judging whether smoothing parameters 
  are on boundary was far too low, so that sps could become so large that 
  numerical instability set in. Fixed. Thanks to Paul Rosenfield.

* p.type!=0 removed in summary.gam (previously deprecated)

* single penalty tensor product smooths removed (previously deprecated).

* gam(...,optimizer="perf") deprecated.

* extra divergence check added to bam gam default gam fitting (similar to
  discrete method). 

* preinitialize and postproc components of extended families are now functions,
  not expressions.

* coefficient divergence check was missing in bam(...,discrete=TRUE) release
  code - now fixed.

* gaulss family link derivatives modified to avoid overflow. Thanks to 
  Kristen Beck for reporting the problem.

* redundant 'n' argument removed from extended family 'ls' functions.

* Convergence checking can step fail earlier in If trial step 
  is no improvement and equal to previous best (to within a tolerance), then 
  terminate with step failure after a few step halvings if situation persists. 
  Thanks to Zheyuan Li for reporting problem. 

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* Tweak to 'newton' to further reduce chance of false convergence at 
  indefinite point.

* Fix to bam.update to deal with NAs in response.

* 'scat' family now takes a 'min.df' argument which defaults to 3. Could 
  otherwise occasionally have indefinite LAML problems as df headed towards 2.

* Fix to `gam.fit4' where in rare circumstances the PIRLS iteration could 
  finish at an indefinite point, spoiling implicit differentiation. 

* `gam.check' modified to fix a couple of issues with `gamm' fitted models, and 
  to warn that interpretability is reduced for such models. 

* `qq.gam' default method slight modification to default generation of reference
  quantiles. In theory previous method could cause a problem if enough 
  residuals were exactly equal.

* Fix to `plot.mrf.smooth' to deal with models with by variables.  

* `plot.gam' fix to handling of plot limits when using 'trans' (from 1.8-16 
  'trans' could be applied twice). 

* `plot.gam' argument 'rug' now defaults to 'NULL' corresponding to 'rug=TRUE'
  if the number of data is <= 10000 and 'rug=FALSE' otherwise.

* bam(...,discrete=TRUE) could fail if NAs in the smooth terms caused data rows
  to be dropped which led to parametric term factors having unused levels 
  (which were then not being dropped). Fixed (in 

* bam(...,discrete=TRUE,nthreads=n) now warns if n>1 and openMP is not 
  available on the platform being used. 

* Sl.addS modified to use C code for some otherwise very slow matrix 
  subset and addition ops which could become rate limiting for 

* Parallel solves in Sl.iftChol can speed up bam(...,discrete=TRUE) with 
  large numbers of smoothing/variance parameters.
* 'gamm' now warns if called with extended families.

* disasterous 'te' in place of 'ti' typo in ?smooth.terms fixed thanks to 
  John McKinlay.

* Some `internal' functions exported to facilitate quantile gam methods 
  in separate package.

* Minor fix in gam.fit5 - 1 by 1 matrix coerced to scalar, to prevent failure 
  in some circumstances. 

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* Export gamlss.etamu, gamlss.gH and trind.generator to facilitate user 
  addition of new location-scale families. 

* Re-ordering of initialization in gam.fit4 to avoid possible failure of
  dev.resids call before initialization. 

* trap in for situation in which all smoothing parameters 
  satisfy conditions for indefinite convergence on entry, with an 
  immediate warning that this probably indicates iteration divergence (of bam).

* "bs" basis modified to allow easier control of the interval over which the
  spline penalty applies which in turn allows more sensible control of 
  extrapolation behaviour, when this is unavoidable. 

* Fix in uniquecombs - revised faster code (from 1.8-13) could occasionally 
  generate false matches between different input combinations for integer 
  variables or factors. Thanks to Rohan Sadler for reporting the issue that 
  uncovered this. 

* A very bad initial model for uninformative data could lead to a negative 
  fletcher estimate of the scale parameter and optimizer failure - fixed. 

* "fREML" allowed in sp.vcov so that it works with bam fitted models.

* 2 occurances of 'return' replaced by (correct) return().	

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* slightly improved intial value heuristics for overlapping penalties in 
  general family case.

* 'ocat' checks that response is numeric.

* plot.gam(...,scale=-1) now changes scale according to 'trans' and 'shift'.

* newton optimizer made slightly more cautious: contracts step if reduction 
  in true objective too far different from reduction predicted by quadratic 
  approximation underlying Newton step. Also leaves parameters unchanged 
  in Newton step while their grad is less than 1% of max grad.  

* Fix to Fisher weight computation in gam.fit4. Previously a weight could
  (rarely) evaluate as negative machine prec instead of zero, get passed to 
  gdi2 in C code, generate a NaN when square rooted, resulting in a NaN passed
  to the LAPACK dgeqp3 routine, which then hung in a non-interuptable way.  

* Fix of 'sp' argument handling with multiple formulae. Allocation to terms 
  could be incorrect. 

* Option 'edge.correct' added to 'gam.control' to allow better correction
  of edge of smoothing parameter space effects with 'gam' when RE/ML used. 	

* Fix to setting of penalty rank in smooth.construct.mrf.smooth.spec. 
  Previously this was wrong, which could cause failure with gamm if the 
  penalty was rank deficient. Thanks Paul Buerkner.

* Fix to Vb.corr call from to ensure that sp not 
  dropped (wrongly treated as scale estimate) when P-REML or P-ML used. 
  Could cause failure depending on BLAS. Thanks Matteo Fasiolo.

* Fix in gam.outer that caused failure with "efs" optimizer and fixed sps.

* Fix to `get.var' to drop matrix attributes of 1 column matrix variables.

* Extra argument added to `uniquecombs' to allow result to have same row
  ordering regardless of input data ordering. Now used by smooth constructors 
  that subsample unique covariate values during basis setup to ensure 
  invariance to data re-ordering. 

* Correction of scaling error in spherical correlation structure GP smooth.

* qf and rd functions for binomial family fixed for zero n case. 

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* Fix of survival function prediction in family. Code used expression 
  (8.8.5) in Klein and Moeschberger (2003), which is missing a term. Correct
  expression is, e.g., (10) from Andersen, Weis Bentzon and Klein (1996)
  Scandinavian Journal of Statistics.  

* Added help file 'cox.pht' for Cox PH regression with time dependent 

* fix of potential seg fault in gdi.c:get_bSb if single smooth model 
  rank deficient (insufficient workspace allocated).

* gam.fit5 modified to step half if trial penalized likelihood is infinite.

* Fix so that bam works properly with drop.intercept=TRUE.

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* bug fix to smoothCon that could generate NAs in model matrix when using bam
  with numeric by variables. The problem was introduced as part of the 
  bam(...,discrete=TRUE) coding. 
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* Added help file ? on the `one standard error rule' for obtaining 
  smoother models.

* bam(...,discrete=TRUE) no longer complains about more coefficients than data.

* 's', 'te', 'ti' and 't2' modified to allow user to specify that the smooth
  should pass through zero at a specified point. See ?identifiability.

* anova.gam modified to use more appropriate reference degrees of freedom 
  for multiple model call, where possible. Also fixed to allow multiple 
  formulae models and to use -2*logLik in place of `deviance' for models.

* offsets allowed with multinomial, ziplss and gaulss families.

* gevlss family implementing generalized extreme value location, scale and 
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  shape models. 

* Faster code used in 'uniquecombs'. Speeds up discretization step in 
  'bam(...,discrete=TRUE)'. Could still be improved for multi-column case.

* modification to 'smoothCon' to allow resetting of smooth supplied 
  constraints - enables fix of bug in bam handling of 't2' terms, where
  parameterization of penalty and model matrix did not previously match 

* clarification of `exclude' argument to predict.gam in help files.

* modification to 'plot.gam' etc, so that 'ylim' is no longer shifted by 

* ylim and ... handling improved for 'fs' plot method (thanks Dave Miller)

* gam.check now recognises RStudio and plots appropriately.

* bam(...,sparse=TRUE) removed - not efficient, because of unavoidability 
  of dense off diagonal terms in X'X or equivalent. Deprecated since 1.8-5.

* tweak to initial.sp/g to avoid infinite loop in s.p. initialization, in 
  rather unusual circumstances. Thanks to Mark Bravington.

* bam and gam have `drop.intercept' argument to force the parametric terms not 
  to include a constant in their span, even when there are factor variables. 

* Fix in Vb.corr (2nd order edf correction) for fixed smoothing parameter case.

* added 'all.vars1' to enable terms like s(x$y) in model formulae. 

* modification to gam.fit4 to ignore 'start' if it is immediately worse than 

* cSplineDes can now accept a 'derivs' argument.

* added drop.intercept handling for multiple formulae (mod by Matteo Fasiolo).

* 'gam.side' fix to avoid duplication of smooth model matrices to be tested 
  against, when checking for numerical degeneracy. Problem could occasionally 
  cause a failure (especially with bam), when the total matrix to be tested 
  against ended upo with more columns than rows.

* 4 occurances of"model.frame") changed to quote(stats::model.frame)
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* fix in predict.bamd discrete prediction code to be a bit more relaxed about 
  use of as.factor, etc in formulae.

* fix in predict.gam handling of 'na.action' to avoid trying to get type of
  na.action from name of na.action function - this was fragile to nesting
  and could cause predict.bam to fail in the presence of NA's.

* fix of gam.outer so that general families (e.g. can have all
  their smoothing parameters supplied without then ignoring the penalties!

* fix in multiple formulae handling of fixed smoothing parameters.

* Fix of bug in zlim handling in vis.gam perspective plot with standard 
  errors. Thanks Petra Kuhnert. 

* probit link added to 'jagam' (suggested by Kenneth Knoblauch).

* 'Sl.' routines revised to allow operation with non-linearly parameterized

* bug fix in Hessian computation in gam.fit5 - leading diagonal of Hessian of 
  log|Hp| could be wrong where Hp is penalized Hessian. 

* better use of crossprod in gamlss.gH
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** "bs" B-spline smoothing basis. B-splines with derivative based penalties
  of various orders.

* 'gamm' now uses a fixed scale parameter in PQL estimation for Poisson and 
  binomial data via the `sigma' option in lmeControl.

* bam null deviance computation was wrong with prior weights (including 
  binomial other than binary case), and returned deviance was wrong for 
  non-binary binomial. Fixed (did not affect estimation). 

* improvements to "bfgs" optimizer to better deal with `infinite' smoothing

* changed scheme=3 in default 2-D plotting to grey scale version of 

* 'trichol' and 'bandchol' added for banded Cholesky decompositions, plus
  'sdiag' functions added for extracting and setting matrix sub- and
  super-  diagonals.

* p-spline constructor and Predict.matrix.pspline.smooth now allow set 
  up of SCOP-spline monotonic smoothers, and derivatives of smooths. Not
  used in modelling functions yet. 

* s(...,bs="re") now allows known precision matrix structures to be defined
  using the `xt' argument of 's' see ? for
  details and example.

* negbin() with a grid search for `theta' is no longer supported - use 
  'nb' instead.

* bug fix to bam aic computation with AR rho correction.
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* bam(...,discrete=TRUE) can now handle matrix arguments to smooths (and hence
  linear functional terms).

* bam(...,discrete=TRUE) bug fix in fixed sp handling.

* bam(...,discrete = TRUE) db.drho reparameterization fix, fixing nonsensical 
  edf2. Also bam edf2 limited to maximum of edf1.

* smoothCon rescaling of S changed to use efficient matrix norm in place of
  relatively slow computation involving model matrix crossproduct.  

* bam aic corrected for AR model if present.
* Added select=TRUE argument to 'bam'.

* Several discrete prediction fixes including improved thread safety.

* bam/gam name gcv.ubre field by "method".

* gam.side modified so that if a smooth has 'side.constrain==FALSE' it is 
  neither constrained, nor used in the computation of constraints for other
  terms (the latter part being new). Very limited impact!

* No longer checks if SUPPORT_OPENMP defined in Rconfig.h, but only if _OPENMP
  defined. No change in actual behaviour. 

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** 'multinom' family implemented for multinomial logistic regression.

* predict.bam now defaults to using efficient discrete prediction methods 
  for models fit using discrete covariate methods (bam(...,discrete=TRUE)). 

* with bam(...,discrete=TRUE) terms like s(a,b,bs="re") had wrong p-value 
  computation applied, as a result of being treated as tensor product terms.

* minor tweak to soap basis setup to avoid rounding error leading to 'approx'
  occasionally producing NA's with fixed boundaries.

* misc.c:rwMatrix made thread safe (had been using R_chk_calloc, which isn't). 

* some upgrading for 64bit addressing.

* uniquecombs now preserves contrasts on factors.

* variable summary tweak so that 1 column matrices in parametric model are 
  treated as regular numeric variables.

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* C level fix in bam(...,discrete=TRUE) code. Some memory was mistakenly 
  allocated via 'calloc' rather than 'R_chk_calloc', but was then freed
  via 'R_chk_free'. This could cause R to halt on some platforms.

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** New "gp" smooth class (see ?gp.smooth) implemeting the Matern 
   covariance based Gaussian process model of Kamman and Wand (2003), and a 
   variety of other simple GP smoothers.

* some smooth plot methods now accept 'colors' and 'contour.col' argument to
  set color palette in image plots and contour line colors. 

* predict.gam and predict.bam now accept an 'exclude' argument allowing 
  terms (e.g. random effects) to be zeroed for prediction. For efficiency,
  smooth terms not in 'terms' or in 'exclude' are no longer evaluated, and 
  are instead set to zero or not returned. See ?predict.gam.

* ocat saturated likelihood definition changed to zero, leading to better 
  comprability of deviance between model fits (thanks to Herwig Friedl).

* null.deviance calculation for extended families modified to make more sense
  when `mu' is the mean of a latent variable, rather than the response itself. 

* bam now returns standarized residuals 'std.rsd' if `rho!=0'. 

* bam(...,discrete=TRUE) can now handle 'fs' terms.

* bam(...,discrete=TRUE) now accepts 'by' variables. Thanks to Zheyaun Li
  for debugging on this.

* bam now works with drop.unused.levels == TRUE when random effects should
  have more levels than those that exist in data. (Thanks Alec Leh) 

* bam chunk.size logic error fix - error could be triggered if chunk.size
  reset automaticlly to be larger than data size.

* uniqucombs can now accept a data frame with some or all factor columns,
  as well as purely numeric marices.

* modified to avoid discretizing a covariate more than once,
  and to halt if a model requires the same covariate to be discretized 
  two different ways (e.g. s(x) + s(x,z)). This affects only 

* Some changes to ziP and ziplss families to improve numerical robustness,
  and to ziP help file to suggest appropriate checking. Thanks to Keren 
  Raiter, for reporting problems. 

* numerical robustness of extended gam methods (gam.fit4) improved for cases 
  with many zero or near zero iterative weights. Handling of zero weights 
  modified to avoid divide by (near) zero problems. Also tests for poor 
  scaling of sqrt(abs(w))*z and substitutes computations based on w*z if 
  detected. Also 'newton' routine now step halves if REML score not finite!

* Sl.setup (used by bam) modification to allow more efficient handling of terms
  with multiple diagonal penalties with no non-zero elements in common, but
  possibly with non zero elements `interleaved' between penalties.

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** 'gam' default scale parameter changed to modified Pearson estimator 
  developed by Fletcher 2012 Biometrika 99(1), 230-237. See ?gam.scale.

** 'bam' now has a 'discrete' argument to allow discretization of covariates
  for more efficient computation, with substantially more parallelization
  (via 'nthreads'). Still somewhat experimental. 

* Slightly more accurate smoothing parameter uncertainty correction. Changes 
  edf2 used for AIC (under RE/ML), and hence may change AIC values. 

* jagam prior variance on fixed effects is now set with reference to data and 
  model during initialization step.

* bam could lose offset for small datasets in gaussian additive case. fixed. 

* gam.side now setup to include penalties in computations if fewer data than 
  coefs (an exceedingly specialist topic). 

* p-value computation for smooth terms modified to avoid an ambiguity in the 
  choice of test statistic that could lead to p-value changing somewhat between 

* gamm now warns if attempt is made to use extended family.

* step fail logic improved for "fREML" optimization in 'bam'.

* fix of openMP error in mgcv_pbsi, which could cause a problem in 
  multi-threaded bam computation (failure to declare a variable as private).  

* Smoothing parameter uncertainty corrected AIC calculations had an 
  indexing problem in Sl.postproc, which could result in failure of bam with 
  linked smooths. 

* mroot patched for fact that chol(...,pivot=TRUE) does not operate as 
  documented on rank deficient matrices: trailing block of triangular factor
  has to be zeroed for pivoted crossprod of factor to equal original matrix. 

* bam(...,sparse=TRUE) deprecated as no examples found where it is really 
  worthwhile (but let me know if this is a problem). 

* marginal model matrices in tensor product smooths now stored in 
  re-parameterized form, if re-parameterization happened (shouldn't change 

* initial.spg could fail if response vector had dim attributes and extended 
  family used. fixed.

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* Generalization of list formula handling to allow linear predictors to
  share terms. e.g. gam(list(y1~s(x),y2~s(z),1+2~s(v)+w-1),family=mvn(d=2))

* New German translation thanks to Detlef Steuer.

* plot.gam now silently returns a list of plotting data, to help advanced 
  users (Fabian Scheipl) to produce customized plot.
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* bam can now set up an object suitable for fitting, but not actually do
  the fit, following a suggestion by Fabian Scheipl. See arguments 'fit' 
  and 'G'. 

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* Korean translation added thanks to Chel Hee Lee.

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* scale parameter handling in edf in logLik.gam made consistent with glm 
  (affects AIC).

* 'bam', 'gam' and 'gamm' modified to often produce smaller files when models 
  saved (and never to produce absurdly large files). Achieved by setting 
  environment of formula, terms etc to .GlobalEnv. Previously 'save' could
  save entire contents of environment of formula/terms with fitted model 
  object. Note that change can cause failure in user written functions calling 
  gam/bam and then 'predict' without supplying all prediction variables 
  (fix obvious).

* A help file 'single.index' supplied illustrating how single index models
  can be estimated in mgcv.

* predict.gam now only creates a "constant" attribute if the model has one.

* gam.fit4 convergence testing of coefs modified to more robust test of
  gradients of penalized dev w.r.t. params, rather than change in params,
  which can fail under rank deficiency.

* mgcv_qrqy was not thread safe. Not noticeable on many platforms as all 
  threads did exactly the same thing to the same matrix, but very noticeable
  on Windows. Thread safe mgcv_qrqy0 added and used in any parallel sections.

* Allow openMP support if compiler supports it and provides pre-defined macro 
  _OPENMP, even if SUPPORT_OPENMP undefined. (Allows multi-threading on 
   Windows, for example.) 

* 'eps' is now an argument to 'betar' allowing some control on how to 
  handle response values too close to 0 or 1. Help file expanded to 
  emphasise the problems with using beta regression with 0s and 1s in 
  the data.

* fix of bug in multi-formula contrast handling, causing failure of prediction
  in some  cases.

* ziP and ziplss now check for non-integer (or binary) responses and produce
  an error message if these are found. Previously this was not trapped and
  could lead to a segfault. 

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** JAGS/BUGS support added, enabling auto-generation of code and data
  required to used mgcv type GAMs with JAGS. Useful for complex random 
  effects structures, for example.

* smoothCon failed if selection penalties requested, but term was unpenalized.
  Now fixed (no selection penalties on unpenalized terms.)
* gam.check would fail for tensor product smooths with by variables - fixed.

* predict.gam would fail when predicting for more data than the blocksize
  but selecting only some terms. Fixed thanks to Scott Kostyshak.

* smoothCon now has an argument `diagonal.penalty' allowing single penalty 
  smooths to be re-parameterized in order to diagonalize the penalty matrix.
  PredictMat is modified to apply the same reparameterization, making it
  user transparent. Facilitates the setup of smooths for export to other 

* predict.bam now exported in response to a request from another 
  package maintainer.

* 1.8 allows some prediction tasks for some families (e.g. to 
  require response variables to be supplied. NAs in these then messed up 
  prediction when they were not needed (e.g. if response variables with
  NAs were provided to predict.gam for a simple exponential family GAM). 
  Response NAs now passed to the family specific prediction code, restoring 
  the previous behaviour for most models. Thanks Casper Wilestofte Berg.

* backend parallel QR code used by gam modified to use a pivoted block

* nthreads argument added to 'bam' to allow for parallel computation 
  for computations in the main process (serial on any cluster nodes).
  e.g. QR based combination of results from cluster nodes is now

* fREML computation now partly in parallel (controlled by 'nthreads' 
  argument to 'bam')

* slanczos now accepts an nt argument allowing parallel computation of 
  main O(n^2) step.

* fix to newton logic problem, which could cause an attempt to use 'score2'
  before definition.

* fix to fREML code which could cause matrix square root to lose dimensions 
  and cause an error. 

* initial.sp could perform very poorly for very low basis dimensions - could 
  set initial sp to effective infinity. 

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* Fix of two illegal read/write bugs with extended family models with no
  smooths. (Thanks to Julian Faraway for reporting beta regr problem).

* bam now checks that chunk.size > number of parameters and resets the 
  chunk.size if not.

* Examples of use of smoothCon and PredictMat for setting up bases 
  for use outside mgcv (and then predicting) added to ?smoothCon.

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* For exponential family gams, fitted by outer iteration, a warning is now
  generated if the Pearson scale parameter estimate is more than 4 times
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  a robust estimate. This may indicate an unstable Pearson estimate.

* 'gam.control' now has an option 'scale.est' to allow selection of the 
  estimator to use for the scale parameter in exponential family GAMs. 
  See ?gam.scale. Thanks to Trevor Davies for providing a clear unstable 
  Pearson estimate example.

* drop.unused.levels argument added to gam, bam and gamm to allow 
  "mrf" (and "re") terms to have unobserved factor levels.

* "mrf" constructor modified to deal properly with regions that contain no 

* "fs" smooths are no longer eligible to have side conditions set, since 
  they are fully penalized terms and hence always identifiable (in theory).

* predict.bam was not declared as a method in NAMESPACE - fixed

* predict.bam modified to strip down object to save memory (especially in 

* predict.gam now has block.size=NULL as default. This implies a block
  size of 1000 when newdata supplied, and use of a single block if no
  new data was supplied. 

* some messages were not printing correctly after a change in 
  message handling to facilitate easier translation. Now fixed.

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* bam modified so that choleski based fitting works properly with rank 
  deficient model matrix (without regularization).

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* fix of 1.8-0 bug - gam prior weights mishandled in computation of cov matrix,
  resulting in incorrect variance estimates (even without prior weights 
  specified). Thanks Fabian Scheipl.

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*** Cox Proportional Hazard family '' added as example of general
  penalized likelihood families now useable with 'gam'.

*** 'ocat', 'tw', 'nb', 'betar', 'ziP' and 'scat' families added for 
  ordered categorical data, Tweedie with estimation of 'p', negative binomial 
  with (fast) estimation of 'theta', beta regression for proportions, simple
  zero inflated Poisson regression and heavy tailed regression with scaled t 
  distribution. These are all examples of 'extended families' now useable 
  with 'gam'.

*** 'gaulss' and 'ziplss' families, implementing models with multiple linear 
  predictors. For gaulss there is a linear predictor for the Gaussian mean
  and another for the standard deviation. For ziplss there is a linear
  predictor controlling `presence' and another controlling 
  the Poisson parameter, given presence. 

*** 'mvn' family for multivariate normal additive models.

** AIC computation changed for bam and gam models estimated by REML/ML
   to account for smoothing parameter uncertainty in degrees of freedom

* With REML/ML smoothness selection in gam/bam an extra covariance matrix 'Vc'
  is now computed which allows for smoothing parameter uncertainty. See
  the 'unconditional' arguments to 'predict.gam' and 'plot.gam' to use this. 

* 'gam.vcomp' bug fix. Computed intervals for families with fixed scale 
   parameter were too wide. 

* gam now defaults to the Pearson estimator of the scale parameter to avoid
  poor scale estimates in the quasipoisson case with low counts (and possibly
  elsewhere). Gaussian, Poisson and binomial inference invariant to change. 
  Thanks to Greg Dropkin, for reporting the issue.

* Polish translation added thanks to Lukasz Daniel.

* gam.fit3 now forces eta and mu to be consistent with coef and valid on
  return (previously could happen that if step halving was used in final
  iteration then eta or mu could be invalid, e.g. when using identity link
  with non-negative data)

* gam.fit3 now bases its convergence criteria on grad deviance w.r.t. model 
  coefs, rather than changes in model coefs. This prevents problems when 
  there is rank deficiency but different coefs get dropped at different 
  iterations. Thanks to Kristynn Sullivan.

* If mgcv is not on the search path then interpret.gam now tries to 
  evaluate in namespace of mgcv with environment of formula as enclosing 
  environment, if evaluation in the environment of the formula fails. 

* bug fix to sos plotting method so that it now works with 'by' variables.

* 'plot.gam' now weights partial residuals by *normalized* square root 
  iterative weights so that the average weight is 1 and the residuals 
  should have constant variance if all is ok.

* 'pcls' now reports if the initial point is not feasible.

* 'print.gam' and 'summary.gam' now report the rank of the model if it is
   rank deficient. 'gam.check' reports the model rank whenever it is 

* fix of bug in 'k.check' called by 'gam.check' that gave an error for 
  smooths with by variables.

* predict.gam now checks that factors in newdata do not contain more
  levels than those used in fitting.

* predict.gam could fail for type "terms" with no intercept - fixed.

* 'bfgs' now uses a finite difference approximation for the initial inverse 

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* Single character change to Makevars file so that openMP multi-threading 
  actually works.  

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* exclude.too.far updated to use kd-tree instead of inefficient search for 
  neighbours. This can make plot.gam *much* faster for large datasets.

* Change in smoothCon, so that sweep and drop constraints (default for bam
  for efficiency reasons) are no longer allowed with by variables and matrix 
  arguments (could lead to confusing results with factor by variables in bam). 

* 'ti' terms now allow control of which marginals to constrain, via 'mc'.
  Allows e.g. y ~ ti(x) + ti(x,z,mc=c(0,1)) - for experts only! 

* re-written to call C code. Around 5-10 times
  faster than old version for large data sets.

* re-write of function used by bam to generate a reduced size 
  model frame for model setup. New version ensures that all factor levels 
  are present in reduced frame, and avoids production of unrealistic
  combinations of variables in multi-dimensional smooths which could occur
  with old version. 

* bam models could fail if a penalty matrix was 1 by 1, or if multiple 
  penalties on a smooth were in fact seperable into single penalties. 
  Fixed. Thanks to Martijn weiling for reporting.

* Constant in tps basis computation was different to published version 
  for odd dimensions - makes no difference to fit, but annoying if you 
  are trying to test a re-implementation. Thanks to Weijie Cai at SAS. 

* prediction for "cc" and "cp" classes is now cyclic - values outside the
  range of knots are wrapped back into the interval.

* ldTweedie now returns derivatives w.r.t. a transform of p as well as 
  w.r.t log of scale parameter phi.

* gamm can now handle 'varComb' variance functions (thanks Sven Neulinger
  for reporting that it didn't).

* fix of a bug which could cause bam to seg fault for a model with no smooths 
  (insufficient storage allocated in C in this case). Thanks Martijn Weiling.

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* Further multi-threading in gam fits - final two leading order matrix
  operations parallelized using openMP. 

* Export of smooth.construct.t2.smooth.spec and Predict.matrix.t2.smooth, 
  and Rrank.

* Fix of of missing [,,drop=FALSE] in predict.gam that could cause problems 
  with single row prediction when 'terms' supplied (thanks Yang Yang).

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* Namespace fixes.


* code added to allow openMP based multi-threading in gam fits (see
  ?gam.control and ?"mgcv-parallel"). 

* bam now allows AR1 error model to be split blockwise. See argument 

* made more efficient (one of two O(np^2) steps removed).

* var.summary now coerces character to factor. 

* bugs fixed whereby etastart etc were not passed to initial.spg and
  get.null.coefs. Thanks to Gavin Simpson.

* reformulate removed from predict.gam to avoid (slow) repeated parser 

* gaussian(link="log") initialization fixed so that negative data 
  does not make it fail, via patching function.

* bug fix in plot method for "fs" basis - ignored any side conditions.
  Thanks to Martijn Weiling and Jacolien van Rij.

* gamm now checks whether smooths nested in factors have illegal side 
  conditions, and halts if so (re-ordering formula can help).
* anova.glmlist no longer called.

* Compiled code now uses R_chck_calloc and R_chk_free for memory management
  to avoid the possibility of unfriendly exit on running out of memory. 

* fix in gam.side which would fail with unpenalized interactions in the 
  presence of main effects. 

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* Examples pruned in negbin, and bam help 
  files to reduce CRAN checking load.

* gam.side now warns if only repeated 1-D smooths of the same variable are
  encountered, but does not halt.

* Bug fix in C code for "cr" basis, that could cause a memory violation during
  prediction, when an extrapolation was immediately followed by a prediction 
  that lay exactly on the upper boundary knot. Thanks to Keith Woolner for 
  reporting this.

* Fix for bug in fast REML code that could cause bam to fail with ti/te only
  models. Thanks to Martijn Wieling.

* Fix of bug in extract.lme.cov2, which could cause gamm to fail when
  a correlation structure was nested inside a grouping factor finer than
  the finest random effect grouping factor. 

* Fix for an interesting feature of lme that getGroups applied to the 
  corStruct that is part of the fitted lme object returns groups in 
  sorted order, not data frame order, and without an index from one order 
  to the other. (Oddly, the same corStruct Initialized outside lme has its 
  groups  in data frame order.) This feature could cause gamm to fail,
  complaining that the grouping factors for the correlation did not appear 
  to be nested inside the grouping structure of the random effects. A 
  bunch of ordering sensitivity tests have been added to the mgcv test suite.
  Thanks to Dave Miller for reporting the bug. 

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*** Fix of severe bug introduced with R 2.15.2 LAPACK change. The shipped
  version of dsyevr can fail to produce orthogonal eigenvectors when 
  uplo='U' (upper triangle of symmetric matrix used), as opposed to 'L'. 
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  This led to a substantial number of gam smoothing parameter estimation 
  convergence failures, as the key stabilizing re-parameterization was 
  substantially degraded. The issue did not affect gaussian additive models
  with GCV model selection. Other models could fail to converge any further 
  as soon as any smoothing parameter became `large', as happens when a 
  smooth is estimated as a straight line. check.gam reported the lack of full 
  convergence, but the issue could also generate complete fit failures.
  Picked up late as full test suite had only been run on R > 2.15.1 with an 
  external LAPACK.

** 'ti' smooth specification introduced, which provides a much better (and 
  very simple) way of allowing nested models based on 'te' type tensor 
  product smooths. 'ti' terms are used to set up smooth interactions 
  excluding main effects (so ti(x,z) is like x:z while te(x,z) is more
  like x*z, although the analogy is not exact). 

* summary.gam now uses a more efficient approach to p-value computation 
  for smooths, using the factor R from the QR factorization of the weighted 
  model matrix produced during fitting. This is a weighted version of the 
  Wood (2013) statistic used previously - simulations in that paper 
  essentially unchanged by the change. 

* summary.gam now deals gracefully with terms such as "fs" smooths 
  estimated using gamm, for which p-values can not be computed. (thanks to 
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  Gavin Simpson).

* gam.check/qq.gam now uses a normal QQ-plot when the model has been fitted 
  using gamm or gamm4, since qq.gam cannot compute corrext quantiles in 
  the presence of random effects in these cases. 

* gamm could fail with fixed smooths while assembling total 
  penalty matrix, by attempting to access non-existent penalty 
  matrix. (Thanks Ainars Aunins for reporting this.)

* stripped rownames from model matrix, eta, linear predictor etc. Saves
  memory and time.

* could switch axis ranges. Fixed.

* plot.mgcv.smooth now sets smooth plot range on basis of xlim and 
  ylim if present.

* formXtViX documentation fixed + return matrix labels.

* fixDependence related negative index failures for completely confounded 
  terms - now fixed.

* sos smooth model matrix re-scaled for better conditioning.

* sos plot method could produce NaNs by a rounding error in argument to 
  acos - fixed. 

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* Predict.matrix.pspline.smooth now allows prediction outside range of knots, 
  and uses linear extrapolation in this case.

* missing drop=FALSE in reTest called by summary.gam caused 1-D random effect
  p-value computation to fail. Fixed (thanks Silje Skår).

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** soap film smoother class added. See ?soap

* Polish translation added thanks to Lukasz Daniel.

* mgcv/po/R-mgcv.pot up-dated.

* plot methods for smooths modified slightly to allow methods to return 
  plot data directly, without a prediction matrix.
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* '...' now passed to termplot by plot.gam (thanks Andreas Eckner).
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* fix to null deviance computation for binomial when n>1, matrix response 
  used and an offset is present. (Thanks to Tim Miller)

* Some pruning of unused code from recov and reTest.

* recov modified to stop it returning a numerically non-symmetric Ve, and 
  causing occasional failures of summary.gam with "re" terms.

* MRF smooth bug. Region ordering could become confused under some 
  circumstances due to incorrect setting of factor levels. Corrected 
  thanks to detailed bug report from Andreas Bender.

* polys.plot colour/grey scale bug. Could ask for colour 0 from colour 
  scheme, and therefore fail. Fixed.

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** summary.gam and anova.gam now use an improved p-value computation for 
  smooth terms with a zero dimensional penalty null space (including 
  random effects). The new scheme has been tested by full replication 
  of the simulation study in Scheipl (2008,CSDA) to compare it to the best 
  method therein. In these tests it is at least as powerful as the best 
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  method given there, and usually indistinguishable, but it gives slightly 
  too low null p-values when smoothing parameters are very poorly identified. 
  Note that the new p-values can not be computed from old fitted gam objects.
  Thanks to Martijn Wieling for pointing out how bad the p-values for regular
  smooths could be with random effects.

* t2 terms now take an argument `ord' that allows orders of interaction to 
  be selected.

* "tp" smooths can now drop the null space from their construction via
  a vector m argument, to allow testing against polynomials in the null space.
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* Fix of vicious little bug in gamm tensor product handling that could have 
  a te term pick up the wrong model matrix and fail. 

* bam now resets method="fREML" to "REML" if there are no free smoothing 
  parameters, since there is no advantage to the "fREML" optimizer in this 
  case, and it assumes there is at least one free smoothing parameter.

* print.gam modified to print effective degrees of freedom more prettily,

* testStat bug fix. qr was called with default arguments, which includes 

* bam now correctly returns fitting weights (rather than prior) in weights 

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* Embarrassingly, the adjusted r^2 computation in summary.gam was wrong
  for models with prior weights. Now fixed, thanks to Antony Unwin.

* bam(...,method="fREML") could give incorrect edfs for "re" terms as a 
  result of a matrix indexing error in Sl.initial.repara. Now fixed. 
  Thanks to Martijn Wieling for reporting this.

* summary.gam had freq=TRUE set as default in 1.7-17. This gave better 
  p-values for paraPen terms, but spoiled p-values for fixed effects in the
  presence of "re" terms (a rather more common setup). Default now reset to

* bam(...,method="fREML") made fully compatible with gam.vcomp.

* bam and negbin examples speeded up

* predict.gam could fail for models of the form y~1 when newdata are supplied.
  (Could make some model averaging methods fail). Fixed.

* plot.gam had an overzealous check for availibility of variance estimates,
  which could make rank deficient models fail to plot CIs. fixed.

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** p-values for terms with no un-penalized components were poor. The theory on 
  which the p-value computation for other terms is based shows why this is, 
  and allows fixes to be made. These are now implemented.

* summary p value bug fix --- smooths with no null space had a bug in 
  lower tail of p-value computation, yielding far too low values. Fixed.

* bam now outputs frequentist cov matrix Ve and alternative effective degrees 
  of freedom edf1, in all cases.

* smoothCon now adjusts on constraint absorption.

* Prediction with matrix arguments (i.e. for models using summation 
  convention) could be very memory hungry. This in turn meant that
  bam could run out of memory when fitting models with such terms.
  The problem was memory inefficient handling of duplicate evaluations.
  Now fixed by modification of PredictMat

* bam could fail if the response vector was of class matrix. fixed.

* reduced rank mrf smooths with supplied penalty could use the incorrect
  penalty rank when computing the reduced rank basis and fail. fixed 
  thanks to Fabian Scheipl.

* a cr basis efficiency change could lead to old fitted model objects causing 
  segfaults when used with current mgcv version. This is now caught.

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* There was an unitialized variable bug in the 1.7-14 re-written "cr" basis 
  code for the case k=3. Fixed.

* gam.check modified slightly so that k test only applied to smooths of
  numeric variables, not factors.


* Several packages had documentation linking to the 'mgcv' function
  help page (now removed), when a link to the package was meant. An alias
  has been added to mgcv-package.Rd to fix/correct these links. 


** predict.bam now added as a wrapper for predict.gam, allowing parallel 

** bam now has method="fREML" option which uses faster REML optimizer: 
   can make a big difference on parameter rich models.

* bam can now use a cross product and Choleski based method to accumulate
  the required model matrix factorization. Faster, but less stable than 
  the QR based default.

* bam can now obtain starting values using a random sub sample of the data. 
  Useful for seriously large datasets. 

* check of adequacy of basis dimensions added to gam.check

* magic can now deal with model matrices with more columns than rows.

* p-value reference distribution approximations improved.

* bam returns objects of class "bam" inheriting from "gam"

* bam now uses newdata.guaranteed=TRUE option when predicting as part
  of model matrix decomposition accumulation. Speeds things up. 

* More efficient `sweep and drop' centering constraints added as default for
  bam. Constaint null space unchanged, but computation is faster.

* Underlying "cr" basis code re-written for greater efficiency.

* routine mgcv removed, it now being many years since there has been any 
  reason to use it. C source code heavily pruned as a result. 

* coefficient name generation moved from estimate.gam to gam.setup.

* smooth2random.tensor.smooth had a bug that could produce a nonsensical
  penalty null space rank and an error, in some cases (e.g. "cc" basis)
  causing te terms to fail in gamm. Fixed.

* minor change to te constructor. Any unpenalized margin now has 
  corresponding penalty rank dropped along with penalty.

* Code for handling sp's fixed at exactly zero was badly thought out, and 
  could easily fail. fixed.

* TPRS prediction code made more efficient, partly by use of BLAS. Large
  dataset setup also made more efficient using BLAS.

* smooth.construct.tensor.smooth.spec now handles marginals with factor
  arguments properly (there was a knot generation bug in this case)

* bam now uses LAPACK version of qr, for model matrix QR, since it's 
  faster and uses BLAS.

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** The Lanczos routine in mat.c was using a stupidly inefficient check for 
  convergence of the largest magnitude eigenvectors. This resulted in 
  far too many Lanczos steps being used in setting up thin plate regression 
  splines, and a noticeable speed penalty. This is now fixed, with many thanks
  David Shavlik for reporting the slow down.  

* Namespace modified to import from methods. Dependency on stats and graphics
  made explicit.

* "re" smooths are no longer subject to side constraint under nesting (since
  this is almost always un-necessary and undesirable, and often unexpected).
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* side.con modified to allow smooths to be excluded and to allow side 
  constraint computation to take account of penalties (unused at present).

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* bam can now compute the leading order QR decomposition on a cluster
  set up using the parallel package.

* Default k for "tp" and "ds" modified so that it doesn't exceed  100 + 
  the null space dimension (to avoid complaints from users smoothing in 
  quite alot of dimensions). Also default sub-sample size reduced to 2000.
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* Greater use of BLAS routines in the underlying method code. In particular 
  all leading order operations count steps for gam fitting now use BLAS. 
  You'll need R to be using a rather fancy BLAS to see much difference, 

* Amusingly, some highly tuned blas libraries can result in lapack not always 
  giving identical eigenvalues when called twice with the same matrix. The 
  `newton' optimizer had assumed this wouldn't happen: not any more.

* Now byte compiled by default. Turn this off in DESCRIPTION if it interferes
  with debugging.
* summary.gam p-value computation options modified (default remains the 

* summary.gam default p-value computation made more computationally 

* gamm and bam could fail under some options for specifying binomial models.
  Now fixed.

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* smoothCon bug fix to avoid NA labels for matrix arguments when 
  no by variable provided. 

* modification to p-value computation in summary.gam: `alpha' argument 
  removed (was set to zero anyway); computation now deals with possibility
  of rank deficiency computing psuedo-inverse of cov matrix for statistic. 
  Previously p-value computation could fail for random effect smooths with 
  large datasets, when a random effect has many levels. Also for large data
  sets test statistic is now based on randomly sampling max(1000,np*2) model
  matrix rows, where np is number of model coefficients (random number 
  generator state unchanged by this), previous sample size was 3000. 

* plot.mrf.smooth modified to allow passing '...' argument.

* 'negbin' modified to avoid spurious warnings on initialization call.

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* fix stupid bug in 1.7-9 that lost term labels in plot.gam.

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* rather lovely plot method added for splines on the sphere.

* plot.gam modified to allow 'scheme' to be specified for plots, to easily
  select different plot looks.

* schemes added for default smooth plotting method, modified for mrfs and 
  factor-smooth interactions.

* mgcv function deprected, since magic and gam are much better (let me know 
  if this is really a problem).  

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* gamm.setup fix. Bug introduced in 1.7-7 whereby gamm with no smooths would

* gamm gives returned object a class "gamm"


* "fs" smooth factor interaction class introduced, for smooth factor 
  interactions where smoothing parameters are same at each factor level.
  Very efficient with gamm, so good for e.g. individual subject smooths.

* qq.gam default method modified for increased power.

* "re" terms now allowed as tensor product marginals.

* log saturated likelihoods modified w.r.t. weight handling, so that weights
  are treated as modifying the scale parameter, when scale parameter is free.
  i.e. obs specific scale parameter is overall scale parameter divided by 
  obs weight. This ensures that when the scale parameter is free, RE/ML based
  inference is invariant to multiplicative rescaling of weights. 

* te and t2 now accept lists for 'm'. This allows more flexibility with 
  marginals that can have vector 'm' arguments (Duchon splines, P splines).   

* minor mroot fix/gam.reparam fix. Could declare symmetric matrix 
  not symmetric and halt gam fit.

* argument sparse added to bam to allow exploitation of sparsity in fitting,
  but results disappointing.

* "mrf" now evaluates rank of penalty null space numerically (previously 
   assumed it was always one, which it need not be with e.g. a supplied 

* gam.side now corrects the penalty rank in smooth objects that have 
  been constrained, to account for the constraint. Avoids some nested 
  model failures.

* gamm and gamm.setup code restructured to allow smooths nested in factors
  and for cleaner object oriented converion of smooths to random effects.

* gam.fit3 bug. Could fail on immediate divergence as null.eta was matrix.

* slanczos bug fixes --- could segfault if k negative. Could also fail to 
  return correct values when k small and kl < 0 (due to a convergence 
  testing bug, now fixed)

* gamm bug --- could fail if only smooth was a fixed one, by looking for
  non-existent sp vector. fixed.

* 'cc' Predict.matrix bug fix - prediction failed for single points.

* summary.gam failed for single coefficient random effects. fixed.

* gam returns rV, where t(rV)%*%rV*scale is Bayesian cov matrix.
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** factor `by' variable handling extended: if a by variable is an
   ordered factor then the first level is treated as a reference level
   and smooths are only generated for the other levels. This is useful 
   for avoiding identifiability issues in complex models with factor by 

* bam bug fix. aic was reported incorrectly (too low). 

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* gam.fit3 modified to converge more reliably with links that don't guarantee
  feasible mu (e.g poisson(link="identity")). One vulnerability removed + a
  new approach taken, which restarts the iteration from null model 
  coefficients if the original start values lead to an infinite deviance.

* Duchon spline bug fix (could fail to create model matrix if 
  number of data was one greater than number of unique data).
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* fix so that 'main' is not ignored by plot.gam (got broken in 1.7-0 
  object orientation of smooth plotting)

* Duchon spline constructor now catches k > number of data errors.

* fix of a gamm bug whereby a model with no smooths would fail after 
  fitting because of a missing smoothing parameter vector.

* fix to bug introduced to gam/bam in 1.7-3, whereby '...' were passed to 
  gam.control, instead of passing on to fitting routines. 

* fix of some compiler warnings in matrix.c 

* fix to indexing bug in monotonic additive model example in ?pcls.

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* Fix for single letter typo bug in C code called by slanczos, could 
  actually segfault on matrices of less than 10 by 10.

* matrix.c:Rlanczos memory error fix in convergence testing of -ve 

* Catch for min.sp vector all zeroes, which could cause an ungraceful 

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** "ds" (Duchon splines) smooth class added. See ?Duchon.spline

** "sos" (spline on the sphere) smooth class added. See ?Spherical.Spline.

* Extended quasi-likelihood used with RE/ML smoothness selection and 
  quasi families. 

* random subsampling code in bam, sos and tp smooths modified a little, so 
  that .Random.seed is set if it doesn't exist. 

* `control' argument changed for gam/bam/gamm to a simple list, which is 
  then passed to gam.control (or lmeControl), to match `glm'.

* Efficiency of Lanczos iteration code improved, by restructuring, and 
  calling LAPACK for the eigen decompostion of the working tri-diagonal

* Slight modification to `t2' marginal reparameterization, so that `main 
  effects' can be extracted more easily, if required.

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* `polys.plot' now exported, to facilitate plotting of results for
  models involving mrf terms.

* bug fix in plot.gam --- too.far had stopped working in 1.7-0.

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* post fitting constraint modification would fail if model matrix was 
  rank deficient until penalized. This was an issue when mixing new t2 
  terms with "re" type random effects. Fixed.

* plot.mrf.smooth bug fix. There was an implicit assumption that the
  `polys' list was ordered in the same way as the levels of the covariate
  of the smooth. fixed. 

* gam.side intercept detection could occasionally fail. Improved.

* concurvity would fail if model matrix contained NA's. Fixed.

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** `t2' alternative tensor product smooths added. These can be used with 

** "mrf" smooth class added (at the suggestion of Thomas Kneib). 
   Implements smoothing over discrete geographic districts using a
   Markov random field penalty. See ?mrf

* qq.gam added to allow better checking of distribution of residuals.

* gam.check modified to use qq.gam for QQ plots of deviance residuals.
  Also, it now works with gam(*, na.action = "na.replace") and NAs.

* `concurvity' function added to provide simple concurvity measures.

* plot.gam automatic layout modified to be a bit more sensible (i.e.
  to recognise that most screens are landscape, and that usually 
  squarish plots are wanted). 

* Plot method added for mrf smooths. 

* in.out function added to test whether points are interior to 
  a region defined by a set of polygons. Useful when working with 

* `plot.gam' restructured so that smooths are plotted by smooth specific
  plot methods.

* Plot method added for "random.effect" smooth class.

* `pen.edf' function added to extract EDF associated with each penalty.
   Useful with t2 smooths. 

* Facilty provided to allow different identifiability constraints to be
  used for fitting and prediction. This allows t2 smooths to be fitted
  with a constraint that allows fitting by gamm4, but still perform 
  inference with the componentwise optimal sum to zero constraints.

* mgcv-FAQ.Rd added.

* paraPen works properly with `gam.vcomp' and full.sp names returned 

* bam (and bam.update) can now employ an AR1 error model in the 
  guassian-identity case. 

* bam.update modified for faster updates (initial scale parameter 
  estimate now supplied in RE/ML case)

* Absorption of identifiability constraints modified to allow 
  constraints that only affect some parameters to leave rest of 
  parameters completely unchanged.  

* rTweedie added for quick simulation of Tweedie random deviates 
  when 1<p<2.

* smooth.terms help file fixed so cyclic p spline identifies as "cp"
  and not "cs"!

* bug fix in `gamm' so that binomial response can be provided as 2 column 
  matrix, in standard `glm' way.

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** Random effect support for `gam' improved with the addition of 
   a random effect "smooth" class, and a function for extracting 
   variance components. See ?random.effects and links for details. 

* smooths now contain extra elements: S.scale records the scale factor 
  used in any linear rescaling of a penalty matrix; indicates 
  whether `plot.gam' should attempt to plot the term; te.ok indicates 
  whether the smooth is a suitable marginal for a tensor product.

* Fix in `gamm.setup' -- models with no fixed effects (except smooths) 
  could fail to fit properly, because of an indexing error (caused odd 
  results with indicator by variables)

* help files have had various misuses of `itemize' fixed. 
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* initialization could fail if response was actually a 1D array. fixed.

* New function `bam.update' allows efficient updating of very large
  strictly additive models fitted by `bam' when additional data become 

* gam now warns if RE/ML used with quasi families.

* gam.check now accepts graphics parameters.

* fixed problem in welcome message that messed up ESS.

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* Bug in cSplineDes caused bases to be non-cyclic unless first 
  knot was at 0. This also affected the "cp" smoother class. Fixed. 

* null.deviance calculation was wrong for case with offset and 
  weights. Fixed.

* Built in strictly 1D smoothers now give an informative error message if 
  an attempt is made to use them for multidimensional smoothing.

* gam.check generated a spurious error when applied to a model with no
  estimated smoothing parameters. Fixed.

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*** Routine `bam' added for fitting GAMs to very large datasets.
    See ?bam

** p-value method tweaked again. Reference DoF for testing now 
  defaults to the alternative EDF estimate (based on 2F - FF where 
  F = (X'WX+S)^{-1}X'WX). `' and `'
  changed to provide this. p-values still a bit too small, but only 
  slightly so, if `method="ML"' is the smoothness selector.

* bad bug in `get.null.coef' could cause fit failure as a result of 
  initial null coefs predicting infinite deviance.

* REML/ML convergence could be response scale sensitive, because of 
  innapropriate convergence testing scaling in newton and bfgs - 

* Slight fix to REML (not ML) score calculation in gam.fit3 - 
  Mp/2*log(2*pi*scale) was missing from REML score, where Mp is
  total null space dimension for model.

* `summary.gam' bug fix: REML/ML models were always treated as if
  scale parameter had been estimated. gamObject should now contain 
  `scale.estimated' indicating whether or not scale estimated

* some modifications to smoothCon and gam.setup to allow smooth
  constructors to return Matrix style sparse model matrices and 
  penalty matrices.  

* fixed misplaced bracket in print.mgcv.version, called on attachment.

* added utility function `ls.list' to give memory usage for elements 
  of a list.

* added function `rig' to generate inverse Gaussian random deviates. 

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* "ts" and "cs" modified so that zero eigen values of penalty
   matrix are reset to 10% of smallest strictly positive eigen
   value, rather than 1%. This seems to lead to more reliable

* `bfgs' simplified and improved so that it now checks the Wolfe 
   conditions are met at each step. No longer uses any Newton steps,
   so if it's used with gam.control(outerPIsteps=0) then it's
   first derivative only for smoothing parameter optimization. 

* `outerPIsteps' now defaults to zero in `gam.control'.

* New routine `initial.spg' gets jth initial sp to equalize 
  Frobenious norm of S_j and cols of sqrt(W)X which it penalizes, 
  where W are initial fisher weights. This removes the need for a 
  performance iteration step to get starting values (so 
  outerPIsteps=0 in gam.control can now bypass PI completely).

* fscale set from get.null.coef (facilitates cleaner initialization).

* large data set rare event logistic regression example added to 

* For p-value calculation for smooths, summary.gam subsamples rows of 
  the model matrix if it has more than 3000 rows. This speeds things
  up for large datasets.

* minor bug fix in `gamm' so that intercept gets correct name, if 
  it's the only non-smooth fixed effect.

* .pot files updated, German translation added, thanks to Detlef Steuer.
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* `in.out' was not working from 1.5 --- fixed.

* loglik.gam now ups parameter count for Tweedie by one to account for 
  scale estimation.

* There was a bug in the calculation of the Bayesian cov matrix, when the 
  scale parameter was known: it was always using an estimated scale 
  parameter. Makes no statistically meaningful difference for a model  
  that fits properly, of course. 
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* Some junk removed from gam object.

* summary.gam pseudoinversion made slightly more efficient.

* adaptive smooth constructor is a bit more careful about the ranks 
  of the penalties.

* 2d adaptive smoother bug fix --- part of penalty was missing due
  to complete line error.

* `smoothCon' and `PredictMat' modified so that sparse smooths can
   optionally have sparse centering constraints applied. 

* `gamm' fix: prediction and visualization from `x$gam' where x is a 
  fitted `gamm' object should not require the random effects to be 
  provided. Now it doesn't.

* minor bug fix: a model with no penalties except a fixed one would fail
  with an index error. 

* `te' terms are now only subject to centering constraints if all 
  marginals would usually have a centering constraint. 

* `te' no longer resets multi-dimensional marginals to "tp", unless 
  they have been set to "cr", "cs", "ps" or "cp". This allows tensor
  products with user supplied smooths.

* Example of obtaining derivatives of a smooth (with CIs) added to 
  `predict.gam' help file.

* `newdata.guaranteed' argument to predict.gam didn't work. fixed.

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* `gamm.setup' made an assumption about basis dimensions which was not 
  true for tensor products involving the "cc" basis. This is now fixed.

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* smooth.construct.tensor.smooth.spec modified, so that 
  re-parameterization in terms of function values is only if it's 
  stable, and by default the parameters are function values with
  even spacing. Otherwise it was possible for tensor products of
  p-splines to fail.

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* `gam' now attempts to coerce `data' to a data frame, if it is not 
  already a list or a data frame, provided that it is already an object 
  that model.frame can deal with. This is to support an undocumented 
  feature of versions prior to 1.5-2 that `data' could actually be 
  something other than a list or data frame. 

* An offset of type "array" could cause gam.fit3 to fail. fixed.

* `variable.summary' bug fixed, (it caused gam(y~1) to fail).   

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* Several exported functions had no usage entries in the help files.
  Everything exported does now.

* `vis.gam' had a bunch of bugs (which could make it fail altogether) 
   as a result of trying to set default conditioning values from the gam 
   object model frame. `gam' and `gamm' now obtain summary statistics of 
   the predictor variables, stored in `var.summary' in the gam object, 
   which `vis.gam' now uses. As a result `vis.gam' `view' and `cond' 
   arguments should now contain original variable names, not model frame 
   term names.

* `data' argument of `gam' no longer stored in the `gam' object, by 
  default to save memory (can restore this --- see `gam.control').

* `summary.gam' failed under na.exclude. Fixed. 

* `mroot' failed on 1 by 1 matrices, Fixed.

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* The stability of the fitting methods had become substantially greater 
  than the stability of the edf calculations after fitting. So it was 
  possible to fit very poor models, and then have non-sensical effective 
  degrees of freedom reported. This has been fixed by using much more stable 
  expressions for edf calculation, when outer iteration smoothness
  selection is employed. (Changes are to gam.fit3, gam.outer and a new
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* edfs in version 1.5-0 were calculated using newton, rather than fisher 
  weights, in the matrix F=(X'WX+S)^{-1}X'WX, the diagonal of which gives 
  the edf array. The problem with this is that it is possible for X'WX 
  not to be +ve definite, and then degrees of freedom can be non-sensical.
  Fisher weights are always used now (although the original problem is 
  exceedingly hard to generate an example of).

* The summation convention code could be *very* memory intensive for cases 
  in which the matrix arguments of a smooth feature many repeated values. 
  Code now fixed to make much more efficient use of any repeated rows in  
  matrix arguments. This enables much larger signal regression problems to 
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  be tackled. 

* Some help file fixes.

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*** Efficient and general REML/ML smoothing selection implemented. 
    Smoothness selection criterion and numerical optimizer are now
    selected using arguments `method' and `optimizer' of `gam', and 
    `gam.method' has been removed.

*** To further enhance stability and efficiency, Fisher scoring is now 
    only used for canonical links, when it corresponds to full Newton. 
    With non-canonical links PIRLS is based on full Newton. 

** Derivative iteration as in Wood (2008) has been replaced by a direct
   implicit function method (which costs no more given Newton based 

** An option `select' has been added to `gam' to allow terms to be
   completely removed from a model by smoothness selection. 

** The shrinkage smoothers "cs" and "ts" have been modified 
   substantially. The Wood (2006, 4.1.6) proposal of adding a 
   small multiple of the identity matrix to the penalty matrix 
   is flawed in that it tends to corrupt small eigen values
   of the penalty matrix for large (dimension) penalty matrices. 
   It is much better to set the zero eigenvalues of the penalty matrix 
   to a small proportion of the smallest +ve eigenvalue, and to 
   use the matrix with the resulting eigen-decomposition as the 
   penalty. This is now done. Thanks to Roman Torgovitsky for 
   reporting the original problem.

** Tweedie family added (including `ldTweedie' function to evaluate 
   log Tweedie densities for powers in (1,2]).

* "ps" "cp" and "cc" smooths can now be supplied with 2 knots to be 
  treated as `endpoints' of the smooths (full set of knots can still 
  be supplied as before).

* The `newton' optimizer was dropping terms when their gradient was 
  below the convergence threshold (and allowing re-entry). This 
  promotes zig-zagging unless the terms are independent. Now only 
  drops terms if gradient and second derivative are very small 
  (so obective is really flat). 

* The adaptive smoothing "ad" class has been greatly simplified and 2D 
  penalty improved. Much faster as a result, and 2D adaptive actually 
  quite good.

* gam.fit3 now checks that the initial PIRLS step produces an 
  improvement in penalized deviance relative to a null model. If not 
  then step halving towards the null model parameter is employed.
  The null model is as close to constant predicted values as the model
  structure allows (it is estimated up front in estimate.gam, to save 

* `gam.side' now takes account of whether the model has an intercept 
   (or the model model matrix column corresponding to an intercept is 
   in the column space of the model matrix of the parametric model 

* The smoothing parameter array returned by `gam' now includes names
  for the smoothing parameters.

* s and te check that `id' is a single element.

* By default, partial residuals are no longer plotted for smooths with 
  `by' variables since they are usually meaningless here (they can 
  be re-instated by argument `by.resids').

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* `min.sp' processing modified to work with `paraPen' argument to 

* vcov.gam defaults to Bayesian covariance matrix.
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* indexing error in `parametricPenalty' corrected.

* plot.gam modified so that page change behaviour is like plot.glm 

* negbin family upgraded to work with (RE)ML.

* `sp.vcov' function added to extract covariance matrix of log 
  smoothing parameters from (RE)ML based fits.

* `power' links now handled by default fitting methods (i.e. gam.fit3)

* `' now expects weights, not sqrt(weights) as the 
   `w' argument (unless `w' is a matrix).

* p-values tweaked again, for slightly better performance with smooths of 
  several variables. Still not quite right.

* record of intial sp's is now carried in `smooth' objects in field 

* ?linear.functional.term error fix.

* memory leak in magic.c:magic fixed --- all fixed smoothing 
  parameters lead to 2 arrays being left unfreed.

* various .Rd file fixes.


* Some minor .Rd file fixes

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* `Predict.matrix2' was not in NAMESPACE: fixed.

* term specific offsets handled properly w.r.t. `by' variables in 
  `smoothCon' (a rather specialized topic!)

* minor doc bug fix for `smooth.construct'.

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*** Model terms can now include linear functionals of smooths, by 
    supplying matrix arguments and matrix `by' variables to model 
    smooth terms. This allows, for example, a model to depend on the 
    integral of a smooth function, or its derivative, or for models to 
    depend on functional  predictors. See ?linear.functional.terms. Main 
    code changes are in `smoothCon' and `predMat'.

** Smooth terms can now be linked in order that they have the same 
   smoothing parameters (and, by default, bases). Linkage is specified
   using the `id' argument to `s' or `te'. Terms with the same `id' value
   will have the same smoothing parameter(s).

** `by' variables can now be factor variables. Also smooth terms with a 
   `by' variable are only subject to a sum-to-zero constraint if it
   is needed for identifiability.

** Argument `paraPen' of `gam' allows (multiple) penalization of 
   parametric model terms. This allows `gam' to fit any model that
   can be expressed as a penalized GLM.

** p-values returned by `summary.gam' now default to a Bayesian 
   approximation which gives (substantially) better frequentist behaviour 
   than the old method.

** The 2 standard error bands for smooths shown by `plot.gam' can now
   include the uncertainty about the overall mean, by default. Such
   intervals have better coverage probability (of their target of 
   inference) than intervals for centred smooths. Argument 
   `type="iterms"' to `predict.gam' will return such standard errors.

** An adaptive smoother class has been added, for smoothing with respect
   to one or two variables: invoked with `s(...,bs="ad",...). 

** `gamm' now supports nested smooth terms, and uses the same, constraint 
   absorbed, parameterization as `gam'.

** `s' and `te' terms accept an `sp' argument setting the term specific 
   smoothing parameters (and over-riding argument `sp' of `gam'). Ignored
   by `gamm'.

** Negative binomial handling changed. `negbin' family added: adapted from 
   MASS to work with gam outer iteration fitting. `gam.negbin' fitting 
   routine added in order to enable use of `negbin' with outer iteration.
   See ?negbin for details. MASS families no longer supported. 
   `nb.theta.mult' removed from `gam.control'.

** The Eilers and Marx style p-spline class is now one of the default 
   smoothing classes, rather than just being an example of how to set up
   a class in the help file. cyclic versions are also available.

** `smoothCon' now handles `by' variables and centering constraints 
   automatically, removing the need for smooth constructors to do so.
   `PredMat' handles `by' variables automatically. Users can over-ride 
   this behaviour when adding smooth classes, if needed - see 

** The interface for adding user defined smooths has been simplified, 
   but this may mean that some user defined classes which worked before
   no longer work: see ?user.defined.smooths

* `smooth.construct' methods are now expected to set default values
   for the penalty order `p.order' and the basis dimension `bs.dim'
   if none are supplied. They should also sanity check supplied 
   values. Previously this was done by `s', but this put unhelpful
   restrictions on new smooth classes.

* `smooth.construct' now expects to recieve `data' and `knots' arguments
   with names corresponding exactly to `object$term'. In addition `data'
   should contain only what is required by `object$term' + a final column
   containing a `by' variable, if present. Predict.mat expects the same of 
   its `data' argument. wrapper functions smooth.construct2 and 
   Predict.mat2 will accept a data frame containing any number of variables 
   -- all that is required is that `object$terms' can be evaluated using 
   it. These functions handle repeat rows in matrix arguments efficiently.

* bug fix in `plot.gam' -- no longer requires to hit return if `select'
  used (ever).

* bug fix in `fixDependence' --- a completely dependent `X2' would not
  be detected, since the first element of R2 would be zero: used first
  element of R1 to set scale instead.

* `' passes corrected n to `magic' so that `n' used in gcv/ubre
   does not include obs with zero prior weight. `gam.fit3' already doing 

* `magic' and `gam.fit3' now allow log smoothing parameters to be a 
  linear transformation of a smaller set of underlying smoothing parameters.

* `mgcv' based fitting has been removed as an option in `gam', as has 
  Pearson based GCV. In consequence `am' argument removed from 
  `gam.method' and `globit' removed from `gam.control'.

* `get.var' now coerces matrix values to numeric vectors, to facilitate 
   the handling of linear functionals of smooths.

* `gam.fit2' has been removed, since gam.fit3 is simply better.

* The default optimizer for the generalized case has been made slightly 
  more efficient (derivative free evaluation of GCV/AIC has been 
  improved). The upshot is that the default is now faster than performance
  iteration in almost all cases (while still being more reliable).

* the `absorb.cons' option has been removed from `gam.control'.

* `' and `' bug fix --- only return 
   family unmodified if all required derivative functions are present.

* `smoothCon' now returns a list of smooth objects to facilitate factor
  `by' variables.

* `smoothCon' makes smooth object labels more informative, if there are 
  `by' variables... this also makes default plots more informative.

* `plot.gam' indicates `by' variables in labels

* `gamm.setup' modified to call `gam.setup' for most of the setup, leaving 
  just the re-parameterization step to do. 

* `gamm' modified to allow constraint absorption (same as `gam')

* `gamm' bug fixed whereby "cc" smooths would get the wrong null space 
  dimension (effect was small, but noticeable, in practice e.g. Cairo 
  temperature example from chapter 6 of Wood, 2006, book).

* print methods now return first argument invisibly as they should.

* code for (very) old style summary removed. 

* `gam.fit3' now traps derivative iteration divergence, and suggests 
  tightening the convergence tolerance `epsilon' in `gam.control'. 
  Divergence can happen for ill-conditioned models if the PIRLS has 
  not converged sufficiently.

* gamm.Rd updated to reflect change to gammPQL in 1.3-28.

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* There was a most annoying warning generated by R 2.7.0 every time `gam'
  was used. Now there isn't.

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* change to DESCRIPTION file.

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* `magic' could segfault if supplied with many constraints and relatively 
   high rank penalties, so that after constriant the penalty  matrix 
   square roots had more columns than rows (never happened in additive 
   model case, but can happen in more general settings). Fixed.

* `gamm' now silently drops grouping factors within the correlation 
   structure formulae that duplicate random effects grouping factors 
   (which automatically act as grouping factors on the correlation 
   structures anyway).

* Some replacement of dubious `as.matrix' calls with use of `,drop=FALSE]' 
  in gamm.r   

1934 1935

** `gamm' modified to call a routine `gammPQL' in place of MASS::glmmPQL. 
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  This avoids some duplication, and facilitates maintainance. 

* Bug fix in `formXtViX' where matrix dimensions got dropped when 
  subsetting thereby messing up variance calculations for gamm fits in 
  which some group sizes were 1. 

1943 1944

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** Fix of nasty bug in large dataset handling with "tp" basis (introduced 
   in 1.3-26). Subsampling code was re-seeding RNG instead of intended 
   behaviour of saving RNG state and  restoring it. Fixed and tested.


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* modification to `gam' so that GCV/UBRE scores reported with all fixed 
  smoothing parameters are consistent with equivalent under s.p. 

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* gam.fit3 modified to test for convergence of coefficients as well
  as penalized deviance, otherwise in extreme cases the derivative 
  iterations can diverge.

* modifications of gam.setup, predict.gam and plot.gam to allow smooths
  to contribute an offset term to the model (offset is returned from 
  smooth.construct or Predict.matrix as an "offset" attribute of 
  model/prediction matrix). This is useful for smooths which have known 
  boundary conditions of some sort.

* PredictMat can now handle NAs in a returned prediction matrix.
* vis.gam can handle NA's in predictions.

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** Modification of large dataset handling for "tp" and "ts" bases. If 
   there are more that 3000 unique covariate combinations for a tprs then 
   3000 combinations are randomly sub-sampled, and used as the initial 
   knots for tprs basis construction. The same random number seed is used 
   every time,  (R's RNG state is unaltered by this). Control of this is 
   usually via the `max.knots' (default 3000) and `seed' (default 1) 
   elements of the `xt' argument of `s'. In consequence, `max.tprs.knots' 
   has been removed from `gam.control'.
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* Modification of `s' and `te' to allow an extra argument `xt' which can 
  contain extra information to pass to the basis constructors for smooths.

* removal of `' from smooth.spec objects - it wasn't used 
  anywhere any more, and is a pain to maintain.

* removal of `full.formula' from the `gam' object - it is no longer used 
  anywhere and requires alot of code to construct.

1987 1988 1989 1990 1991 1992

* A bug in `' caused prediction to fail for `s' terms 
  of 4 or more variables, unless the `m' argument was supplied explicitly 
  (and was large enough for the number of variables). Fixed. 

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* summary.gam modified so that it behaves correctly if fitting routines 
  detect and deal with rank deficiency in parameteric part of a model.

* spring cleaning of help files.

* gam.check modified to report more useful convergence diagnostics.

** `model.matrix.gam' added.

** "cr" basis constructor modified to use the same centering conditions 
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  as other bases (sum to zero over covariates, rather than parameters 
  sum to zero). This makes centred confidence intervals for smooths, of 
  the sort used in plot.gam, behave in a similar way for all bases. With 
  the old "cr" centering constraint there could be high negative 
  correlation between coefficients of a centered smooth and the intercept: 
  this could make centred "cr" smooth CIs wider than CIs for other bases 
  (not really wrong, but disconcerting).  

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* step size correction bug fixed in gam.fit3. `Perfect' convergence could
  cause the divergence control loop to fail: the divergence control loop
  was asking for near strict decrease in the penalized deviance, which 
  could be numerically impossible to achieve if the algorithm had actually 
  converged completely.... fixed.  

* minor doc bug fixes.

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* Cheap but unneccesary code added to gdi.c and magic.c to stop 
  inappropriate uninitialized variable warnings from some compilers.

** Bad bug in gam.fit3 fixed. Prior weights of zero were not handled 
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  correctly - prior weight vector should have been subsetted before
  gdi call, but this didn't happen. Result was infinite derivatives
  and fit failure. fixed.

* Related bug in gam.fit3: dropped observations not handled correctly 
  in deviance calculation, which can result in inappropriate step 
  halving. fixed.

* inner loop 3 in gam.fit2 and gam.fit3 modified so that step halving 
  continues until penalized deviance is at worst non-increasing. 

* stupid bug in summary.gam, p-value calc. fixed.

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* minor bug in - edf array not passed to `mgcv.find.theta'
  if method "perf.magic" used - so wrong EDF used for theta estimation 
  with neagative binomial. fixed. 

* Theta estimate added to family object of fitted gam if negative binomial 

* extract.lme.cov(2) modified to allow use with single level grouping 
  factors (not really sure when this is useful)

* bug in gam4objective called when using gam.method(outer="nlm") - never 
  used GCV.

* fixed bug in `newton' whereby immediate convergence actually caused
  routine to fail.

* modified `smoothCon' and `predictMat' so that `qrc' attribute always
  created if constraint absorption used, even if there are no constraints.
  This attribute can then be used to test that there are no unabsorbed 
  constraints (e.g. in `gam.outer').

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* Bad bug in `newton' - step halving set up so that step *never* 
  accepted (it still beat all previous methods in simulations)

* Minor bug in `newton' step limiting of Newton steps reduced step
  to max component 1, rather than `maxNstep'. 

* Some documentation fixes

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*** SUBSTANTIAL CHANGE: 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* additive model 

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

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

* 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 and 
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  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 
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  smoothing parameters when using `outer' iteration in the generalized 
  case. This can speed up analyses that rely on several refits of the same 

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* 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. 

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

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

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

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** `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)
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  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. 

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

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

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* 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). 
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* 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

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* some doc changes

** The p-values for smooth terms had too low power sometimes. Modified 
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  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.

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* 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 because of a bug in handling the 
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  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).

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* added package doc file

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

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* 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,
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  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

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* 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).

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

* and 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, 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)

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* 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. 

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* 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
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 set up to deal properly with user defined pdMat classes (latter 
 confirmed by DB).
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** Improvements to anova and summary functions by Henric Nilsson 
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  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 
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  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.


* inst/CITATION file added. Some .Rd fixes

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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.