-------------- GAMLSS NEWS-------------------------------------------
gamlss is a R package implementing the Generalised Additive Models for
Location Scale ans Shape (GAMLSS) of Rigby and Stasinopoulos (2005),  
Appl. Statist., 54,  pp. 507-554.

The book 
"Flexible Regression and Smoothing: Using GAMLSS in R" 
explains how the models can be used in R.

The book 
"Distributions for modeling location, scale and shape: Using GAMLSS in R" 
explains the explicit and generated distributions available in the 
package gamlss.dist  

More more information about books and papers related to GAMLSS can be found in

http://www.gamlss.com/

Question or suggestions about GAMLSS can be send to

   d.stasinopoulos@londonmet.ac.uk
   or
   r.rigby@londonmet.ac.uk
--------------------------------------------------------------------------------
Version 5.1-6
   1. gamlss 
   
        i) predict.gamlssML() with the option `newdata' was coming back with a vectors of length 1. This is corrected now. The length now is the number of rows in newdata. 
        
   2. gamlss.dist 
        i) a correction for pSEP2 so the cdf is never bigger that .9999         ii) ST1 in the help file the argument "..."" was deleted since did not exist the function
   
   3. gamlss.foreach A new package is create for parallel computations. At the          moment it  has the following functions:
   
          i)  centile.boost() for centiles confident interval
          
          ii)  pc() for principal component regression and 
          
          iii) fitRolling() for time series rolling fitting        

Version 5.1-5

   1. gamlss i) the code in the example for the centiles.pred() function has been changed
   
           ii) the function random() allows now prediction.
           
           iii) the argument "level"" has been added to the function re(). 
           It allows uses to get the marginal contribution of the random effect              factor when use predict().
           
           vi) suppressWarnings(Fun <- splinefun(x, fv, method="natural"))
              was added in gamlss.pb() gamlss.pbc(), gamlss.pbm() and gamlss.pbz()                to avoid warnings when the values of x-variable are not unique 
              
            v) The argument 'xvar' in functions centiles(), centiles.fan()
             centiles.split(), and centiles.com() is not needed any more provided 
             that the function can find it from the fitted model 
             (As suggested by Tim Cole).
             
            vi) The lms() function has been modified to so the "call" represent                   closer the real call of the fitting. For example, while before was                 shown y~pb(x) now the real names will be shown i.e. head~pb(age) 
            
 2. gamlss.dist i) the PIG2 distribution is added            

Version 5.1-4
  1. gamlss i) logSurv0() is added 
            ii) new arguments in the histDist() were added to help the figures in                   book 2 (dittributions)
           iii) the function pdf.plot() is updated
  
  2. gamlss.dist i) PARETO1o is added to help to estimated the power parameter of the tail of the data
                 ii) some options for the plotting distribution function (used for the second book of GAMLSS are updated)
                 
  3. gamlss.data i) the InfMort data set is added 

Version 5.1-3

 1. gamlss i) ri() has a new argument x.xars which should be used if prediction                    is to used later on. 
           ii) The functions logSurv(), loglogSurv(), loglogSurv1(), loglogSurv2() and  loglogSurv3() are rewriten and new extra argument "title",`'lcol' and `ltype'  have  been added
           iii) the function ECDF() and loglogplot() have been added.
 
 2, gamlss.dist i) a mistake in the formula of the sigma for BE distribution in the help file is corrected thanks to Stephen Cromie  
       ii) some new functions for skewness and kurtosis (related to the new distrinution  book of GAMLSS) have been added. The functions are   
                  a) momentSK() 
                  b) centileSK()
                  c) centileSkew()
                  d) centileKurt()
                  e) theoCentileSK()
                  f) plotCentileSK()
                  g) checkMomentSK()
                  j) checkCentileSK()
    [Comment by Mikis Stasinopoulos: I would like (no) to thank the two                         contributors who answer my question in r-help-request@r-project.org 
        on the 16-2-2019.  
       Both answer were completely useless and one on the border of rudeness]   


Version 5.1-2

 1. gamlss  i) The function getQuantile() is added 
            ii) the function pb() includes now the argument `max.df' for                               setting a fix maximum number for the degrees of freedom in                             order to avoid overfitting.
          iii) pbc() has now `max.df' and the prediction is done similar to 
                 pb() by using a generated cubic spline approximation function.
                 Extrapolated prediction values are not a good idea for pbc()
                 since are are using natural splines. A warning is given when 
                 this happens
          iv) GAIC.table() now produce a table of different models against different                 penalties       
          v) the function make.link.gamlss() has two new link function "(0,2]" and 
              "[-1,1]"       
 2. gamlss.dist 
           i) the Discrete Burr type XII distribution, DBUR12(), is added.
           ii) Stanislaus Stadlmann and Lens Lichter have added mean() a
           and variance()  functions to the following distributions:
               BE(), BEo(), EGB2(), exGAUS(), EXP(), GA(),GEOM(), GEOMo(),
               GG(), GU(), GT(), IG(), IGAMMA(), JSU(), JSUo(), LG(),LO(),
               LOGNO(), NO(), NO2(), PARETO(), PARETO2(), PE(), PE2(), PO(),
               RG(), SN1(), SN2(), SST(), TF(), TF2(), WEI(), WEI2(), WEI3(),
               YULE(), ZAGA(), ZAIG()
               
 3. gamlss.data
           i) the data set aircond (used in the second book) is added   
           
 4. gamlss.tr 
           i) the trun.r() is ammended so for discrete distributions the randomised                data are always in a< y <b.

Version 5.0-9

1. gamlss.dist  i)  NET(): the NET distribution was revised: 
            a) The pNET(), qNET() and rNET functions were rewritten. 
            b) The NET() functions was ammended so that gamlssML() will work                   with it
             ii) The function qZAGA has benn corrected by Almond Stocker.
             iii) The distribution SIMPLEX is added

1 gamlss   pvc(): the plot of object created  by pvc(x, by=f) when f is factor             now prints the levels of the factor in the title i.e. 
           plot(getSmo(model), factor.plots = TRUE)  
         
      wp(): worm plot  did not work if for example histDist(parzen$snowfall) is        used  instead of  histDist(snowfall, data=parzen). This is corrected now.
          
      pbm(): the non-linear degrees of freedom  exported by the function have           been  corrected. 
         
      histDist(): the `freq' argument did not work if the `data' argument was        used. This is corrected now.
        
      gamlssML(): small ammendement so can work with fixed parameters distributions like NET(). Also the predict.gamlssML() is added
        
      VGD() and TGD(): now print the colunm name as "pred.GD"
        
      Three new functions are introduced 
      
              (a) gamlssMLpred() and 
              
              (b) fitDistPred()
              
              (c) chooseDist()
              
         gamlssMLpred() and fitDistPred() are using useing `newdata'. For example the          gamlssMLpred() fits the training data and uses the newdata to evaluate the           prediction global deviance. fitDistPred() choose the model with best 
         prediction global deviance. 
         chooseDist() is using the function update.gamlss() to fit all relevant
         parametric gamlss.family distributions to a fitted gamlss model. It can be           slow but its parallel version should be resonable.
        
       model.frame.gamlss() has been amended after suggestions on stackoverflow by
        RolandASc and Hedgehog
        
       rqres() was amended for the discrete distributions so to produce a finite            quantile residuals if the probability integral transform function produced            values close to zero.
      
       predictAll() is amended to cope with the NET distribution (where some of the parameters are fixed)  

-------------------------------------------------------------------------------
Version 5.0-6

1 gamlss Correcting a problem reported in R Under development (unstable) (2017-09-12 r73242)


Version 5.0-5

1.  i) gamlss pb(), pbm(), pbz(), and pbc() now stop if x is a matrix, it gives an  informative error.
    ii)  The predict() function  of the re(ramdom=~1|f) is working now (Note that random() still do not allow prediction) 
    iii) A bug in pb(), pbm() and pbz() is corrected. The bug effected the prediction. Thanks to Lisa Schlosser for reporting it. 


2. gamlss.dist
     i) the link of nu in NBF() have changed to "log"


Version 5.0-4

1. gamlss
     i) the function devianceIncr() is introduced to calculate the global deviance increment. 
     ii)  An argument for changing the size of the axis is added in wp()
     iii) The method of calculating the average of rqres.plot() has changed 
          In fact now the plot.type has two new arguments "few" and "all".
          "few" plots different wp's or QQ's while "all" plots all the wp or QQ              plots on the same graph.
      iv) The function get.object() within the gamlss() function have changed.
          The reason is that in a model like 
          
          u1 <- gamlss(y~pb(x1)+pbc(x4)+pb(x5)+ga(~s(x2,x6)), data=usair)
          
           changed the order of smoothing terms in the formula resulting to     an incorrect term.plot().
           

2. gamlss.dist
     i) Double binomial distribution is introduced
     ii) rechecking of the discrete distributions resulted to some minor changes
         to several distributions. 
     iii) An "if" "else" statment was replaced in PE and PE2 distributions with ifelse because it was causing problems with gen.Family (thanks to Francimário who report it)      
      

Version 5.0-2
1. gamlss  
       i) The function lms() has been changed. Now the different fittings do not start from the previous values of the normal model. This was found to fail in cases where the number of observations was relative small (thanks Tanmay Nath for point  out to us).
      ii) ri() is amended so it can be used when the number of variables are larger than the number of observations. In such case we found that maybe the starting value for lambda (argument start) may have to change from 10 to a bigger value say 1000?.   
      iii) gamlssML() accepts both Y or formula Y~1 (and some bug which had to do with not using the formula when used within another function is corrected)

2. gamlss.add 
        i) Marco Enea have done some adjustments on the way data is dealt by  ga() and ba()
       ii) the degrees of freedom of tr() function now are corrected. They are the number of leaves + the number of beak points. This effects the results on the page 309 of the book.
       
3. gamlss.dist 
       i) The discrete distribution BNB() (Beta negative binomial) is introduced together with its zero adjusted and zero inflated versions ZABNB() and ZIBNB().
      ii) The zero adjusted and zero inflated versions  of the Sichel (SICHEL) distribution are introduced ZASICHEL() and ZISICHEL().
     iii) The zero adjusted PIG is introduced ZAPIG()
      iv) the cdf of the GG distribution had an error when nu<0 (it was spotted by Felix Hunschede) and now is corrected.


Version 5.0-1
1. gamlss i)  term.plot() is amended when call vis.lo()
          ii) gen.likelihood(): a bug preening the function to work for NA                       coefficient is corrected 
          iii) dependence is now from R version 3.3.0 since the function 
               startsWith() used by both gen.likelihood() and getPEF()  is new on                 the R 3.3.0 version as pointed out by Uwe Ligges.
          iv) fitDist() A bug when weights were used is corrected 
           v) gamlss() the iterative weights (wt) and fitted values (fv)
              in glim.fit are prevented to go to Inf or very extreme values.
          vi) gamlssML() the weights arguments is fixed. Now the beginning of 
              the function is similar to gamlss()
             
2. gamlss.add i) Marco Enea done some adjustments on the way data is dealt by ga()               and ba()
              ii) the degrees of freedom of tr() function now are corrected. They               are the number of leaves + the number of beak points. 

Version 5.0-0
1. gamlss i) gen.likelihood() :a bug in the function gen.likelihood() which was failing                for binomial family distribution has been corrected 
             (Thanks to Jason Martinez who point it out) 
             Also the function now can cope with fixed distribution parameters
             (Thanks to Tim Cole and Lynn Sleeper)
        ii)  lms(): a bug in the lms() function which effect the transformation of the 
             x-variable is corrected.
       iii)  fitted.plot() the condition for line.type has been amended 
        iv)  gen.likelihood(): The function did not work with "data=an.omit(data)" 
             (Thanks to Jonathan Williams)
         v)  dtop() the formula of Jager and Wellner (2004) was added on.
         vi) the function getPEF() for getting the partial effect of continuous variables              is introduced
        vii) predictAll() has changed. Now it includes the method of obtain the predictive
              values (and its se's) using weights
       viii) histDist() more arguments are added for changing the colour, type and width of              the fitted distribution line.
         x) pb() fixing the degrees of freedom is amended it now works with log-lambda and             also gives a warning if the df desired df's are not acheive.
        xi) the global deviance tolerance has now set to infinity in gamlss.control(), 
             i.e gd.tol=Inf. This allows models with estimated smoothing parameters to be                  more flexible in each iteration.  
        xii) vcov() and summarry() now can cope with fixed distribution parameters. Note that 
             robust variance covariance i.e. rvcov() is not working with fixed parameters.
        
2.  gamlss.dist 
        i) the  EGB2 and  GB2 distribution had the link function for sigma changed from                 identity to log 
        ii) the Negative Binomial Family (NBF) has been added
        
3.   gamlss.data 
        i) the film90 data are changed to preserve anonymity  
       ii) the eu15 data set is added.
       
4.   gamlss.add
        i) the ga() have changed (thanks to Daniil) and it lot faster
       ii) the ba() function is added interfacing with bam() of package mgcv of Simon Wood
       
5.   gamlss.add2 is created for extra smoothing terms within GAMLSS.
        i) the ma() for fitting MARS models using the earth packages is introduced.
        
Version 4.4-0

1. gamlss   i) the term.plot() did not plot (by mistake) a gam object. 
               This is now corrected 
           ii) the vis.lo() is introduced for plotting fitted loess curves 
          iii) VC.test() for Vuong and Clarke test is introduced (use with caution) 
           iv) the function stepVGDAll.A() is introduced. 
            v) Q.stats() function is amended so the number of observations in each intervals add up to                the total number of observations (that was a bug)
           vi) the pcat() function for reducing the levels of a categorical 
               variable is introduced.
          vii) the pbc() a more robust version of cy() is introduced.
2. gamlss.mx
            i) the function plotMP() is added
3. gamlss.data
            i) the Tokyo rainfall data is included in gamlss now as an example of cycle 
               P-spline  smoothing
           ii) the data set grip (hand grip strength) is added (for centile estimation)
          iii) the data set leukemia is added (for random effect analysis)
            v) the data oil is included

Version 4.3-7

1. gamlss i) the function random() had a bug which did not take the df's. This is now     corrected
          ii) when the functions pb(), pbm(), pbz(), cy() and random() were used a list was created. Now R object are created of class "pb", "pbm", "pbz", "cy" and"random" respectively. Those objects can be accessed after the fit using the function getSmo(). The methods print(), plot(), fitted() and coef() can be applied to them
         iii) the function histSmo() has been revised
          iv) the function getZmatrix() is created to facilitate fitting smoother using the re() function.
           v) The function lo()  has been revised (it was failing under 3.2.3 (2015-12-10)) 

2. gamlss.data i) the glasses data set, a nice example of a mixture distribution, is added
               ii) the respInf data set: an example of  binary random effects
               iii) the meta data set: an example of binomial non=parametric random effect model



Version 4.3-6
1. gamlss i) the functions add1TGD() drop1TGD() and stepTGD() are added to the namespace
             (something which was missed with the version Version 4.3-5)
          ii) the function stepGAICAll.A() can use parallel computations
          iii) the function get.smoothers() within gamlss() has been modified to 
               preserve the order of the terms in the formula (previously it was taking
               the order of the .gamlss.sm.list). This was rather confusing when you are
               trying to retrieve information on the smoothers using for example 
               the function getSmoo().
          iv) The function pbz() is introduced. The function is similar to pb() 
              but allows the fitted smoother to go to a constant rather than    linear curve which is useful for model selection
Version 4.3-5
1. gamlss  i) the functions   VGD(), VGD1(), VGD2(), TGD() have been removed and 
               replaced by the functions 
                   1) gamlssVGD,  
                   2) VGD(),  
                   3) getTGD(), 
                   4) TGD(),
                   5) gamlssCV(), 
                   6) CV(). 
               The function gamlssVGD() fits a gamlss model to the training data. 
               The results is a gamlss objects with three extra components: 
                  a) VGD the global deviance applied to the validation data sets. 
                  b) predictError  
                  c) residVal the residuals for the  validation  data set. 
               The function VGD() extract the validated global deviance. 
               The function getTGD() assumes that the users already have fitted 
                a models and now he/she interested  to evaluate the global deviance 
                at a new (validation or test) data set. 
               TGD()}  extracts the validated/test global deviance,
               The function gamlssCV() performs a k-fold cross validation on a 
               gamlss models. 
               The function CV()} extract the cross validated  global deviance. 

          ii)  The functions dropterm.gamlss(), addterm.gamlss() and stepGAIC() can 
               use parallel computing. stepGAIC() no longer has the argument additive.
                
         iii) The functions drop1All() and add1All() are added while the function 
              stepGAICAll.B() works now with parallel. 

         iv)  The function rqres.plot() has modified to allow  arguments for the wp()
              function.
      
          v) pb(): an extra condition was added if the number of distinct points of the   
              x-variable  is less the number of interval (in control) then the number of 
              intervals is set  to number of   distinct values of x (thanks to Juliane Manitz 
              for point out the problem) 
          
         vi) rqres() function is modified to produce the correct randomised residuals for 
             mass point 1 in the INFBE and INFBE1 distributions. 
             Also rqres1() (a hidden function as rqres()) is created  for allowing to fix the 
             seed in the randomisation. To use the function use gamlss:::rqres1(). 
               
        vii) pbm(): is using svd now like pb() 

        viii) term.plot() the  warning when nn() or ga() was used (when ylim is calculated for 
               "common") is suppressed.

         ix) A lot of functions within gamlss have the arguments "what" as a way to refer to a specific
            parameter of the distribution. We decide to include also the argument "parameter" as more
            intuitive way of referring to a specific parameter. In his version if say `parameter="mu"' 
            is set then take over from the  "what" argument. The function effected are 
             fitted.gamlss(), coef.gamlss(), lp(), fv(), model.frame.gamlss(), terms.gamlss(),
             model.matrix.gamlss(), formula.gamlss(), update.gamlss(), dropterm.gamlss(),
             addterm.gamlsss(), stepGAIC(), edf(), lpred(), confint.gamlss(), fitted.gamlssML(),
             getSmo(), predict,lms(), plot2way(), predict.gamlss(),stepGAIC.VR(), stepGAIC.CH().

          x) The function ri() (for ridge regression) is rewriten. Now the function allows different type of penalties. For example using the argument "Lp=1" or "Lp=0" a lasso or a selection of linear terms can be done respectively.    

1. gamlss.dist A bug in is pBB() is fixed thanks to Jonatan Kallus.


Version 4.3-3

1. gamlss
           i) The glim.fit() function within gamlss() has a line added to 
              prevent the iterative weighs wt to go to Inf. 
          ii) The tp() function within lms() and quantSheets() has changed name
              and modified slightly 
         iii) The vcoc.gamlss() has the warnings changed and allows if the 
              inverse of the Hessian (R) fails to recalculated using different 
              ().
          iv) The output of the term.plot() function has changed.  
           v) IMPORTANT: pb() has a new version which is faster than the old one.
               Th old pb() function is renamed and called pbo()
          vi) The summary.gamlss() functions instead of giving an R warning for
                additive terms it gives a "Note" in the output.
         vii) Q.stats() has been modified to .n.iter=5 will work with small 
                data

2.  gamlss.demo 
           i) The Locmean(), Locpoly(), WLocmean() and  WLocpoly()  functions 
              were moved from the package gamlss.util to here since they are 
              used only for the demos.
          ii) The function demo.LocalRegression() is added

3. gamlss.dist
            i) The discrete distribution double Poison DPO() is added.
            ii) a modification on plotZAGA() is added 

4. gamlss.util 
           i) garmaFit() is modified to allow binomial (and binary) fits 
              (Thanks to Matthias Schmid for point out to us). 
          ii) scattersmooth() the argument "cols" was added to allow different 
              schemes of colours. This also allow the package not to depend to 
              the package colorspace even though the plot is not looking as good.
              If you have the colorspace in you version of R you can still use 
              the old scheme, see the example in scattersmooth().
          iii) plotSimpleGamlss() : had a minor amendment thanks to Michael Guan. 
5. gamlss.add
            i) the function plotNN() is added for plotting fitted neural netwarks 
--------------------------------------------------------------------------------
Version 4.3-1

1. gamlss
          i)  the functions centiles(), centiles.fan() and centiles.com() had 
              some of the plotting options changed
          ii) the lms() the function has changed with some extra arguments added,
              and a with an extra predict.lms() method. Now the lms object 
              (created by lms()) can be used with centiles.pred().
          iii) z.scores() function for lms object is added to simplify getting 
               the z.scores for new observations
          iv) the function quantSheets() is added. It uses the method of 
              Schnabel and Eilers (2013) to create quantiles curves for 
              centile estimation. The created object has print(), fitted(),
              predict() and  resid() methods. 
           v) the functions z.scoresQS() and findPower() are also added to 
              assist centile estimation modelling using quantSheets().

2. gamlss.dist
          i) the function flexDist() is moved from gamlss.util to gamlss.dist
         ii) a bug is fixed in the qZAGA() and qZAIG() functions
  

--------------------------------------------------------------------------------
Version 4.3-0

1. gamlss
          i)   kri is added as another smoother
          ii)  rvcov() the argument hessian.fun is added
          iii) fp() now saves the lm fitted models and it can be access using 
               getSmo()
          vi)  ra() and rc() are gone to be replaced by re() and interface for calling mle() within gamlss


2. gamlss.dist

          i) Family() is amended to save a function rather just a list
          ii) all the FORTRAN source files have now translated to C thanks to 
              Marco Enea
          iii)  ST3C a version of ST3 written in C rather in R is added thanks to 
               Alexios Ghalanos

3, gamlss.add 
          i) fk() now does not print the results in each iteration (it was left 
              by mistake in the last version) 
          ii) ga() ,tr() and nn() now can take bigger formula.


Version 4.2-8

1. gamlss

       i) pb(), cy(), ri(), and pvc(): now  divide with the correct number of observations if prior weights with zeros are used.

       ii) pbm() is added as a new additive function allowing monotonic curve smooth fitting
    

2. gamlss.add
       i) the function tr() is added for fitting decision trees within gamlss

3. gamlss.data

        i) the new data set rent99 is added
        ii) the data mviBig is added 


Version 4.2-7

1. gamlss 
       i) gamlssML(): now allows the fitting  binomial data (sorry it never checked before) and the use of formula in the specification of the model (e.g, y~1) to be consistent with gamlss(). Note that explanatory variables will be ignored if used with gamlssML().
       ii)  .gamlss.multin.list is now on NAMESPACE
       iii) the functions vcov.gamlss() and summary.gamlss() have now an extra argument 
            Hessian.fun with two alternatives "R" and "PB" in order to calculate the Hessian.
            The "R" option uses the R function optimHess() while the "PB" uses a local function 
            based a function of Pinheiro and Bates in nlme package.
       iv)  the function plot2way() is introduced as a way of plotting a two way interaction between two
            categorical variables (factors).
        v)  the functions vcov.gamlss() and gen.likelihood() now works even if NA are in the coefficients 
             of the beta the paraneters
       vi)  The functions loglogSurv1(), loglogSurv2(), loglogSurv3(), loglogSurv() and logSurv() are
            introduced as a way of exploring  the tail behaviour of a distribution.
       vii) AIC(), GAIC() and  extractAIC() have a new argument "c" which if k=2 and c=TRUE gives the corrected AIC that is, AICc (suggested by Mario Alvarado).

2. gamlss.dist
       i) The use of the distributions BCCGo(), BCPEo() and BCTo()  (which are identical to BCCG, BCPE 
          and BCT respectively apart from the fact that they using log link for mu) had some side effects
          because the equivalent d, p, q and r functions did not exist. In this version to avoid the 
          problem we have added the functions dBCCGo(), pBCCGo(), qBCCGo(), rBCCGo(), dBCPEo(), 
          pBCPEo(), qBCPEo() and rBCPEo(), dBCTo(), pBCTo(), qBCTo() and rBCTo(). Those functions
          are identical to  d, p, q, and r functions of  BCCG(), BCPE() and BCT(). 
       

3. gamlss.tr
        i) functions fitTail() and fitTailAll() are introduced for fitting truncated distributions to 
           the tails and creating Hill type of plots respectively.

4. gamlss.demo
      i) The distributions SN1, SN2, LOGITNO, LOGNO2, TF2 and SST are added to the demos



Version 4.2-6

1. gamlss 
     i)  LR.test() a small amendment when checking whether it is  gamlss model
     ii) gen.likelihood()  a small amendment to make sure that it gets the correct link functions
     iii) the functions get.K() and rvcov() are introduced for robust (sandwich) standard errors
     iv)  A new argument "robust" is introduced for the methods
           a) vcov.gamlss()
           b) summary.gamlss()
           c) confint.gamlss()
      v) getSmo() is introduced for get information on smoothing terms
      vi) lms() a bug on the previous version is corrected and a additional feature is added with argument
          "trans.x".  With "tans.x=TRUE" the function looks for a transformation in the x-variable.
2. gamlss.add
      i)  fitFixBP() is named now fitFixKNots()
      ii) attach() data is taken out from fitFixKnots(), fitFreeKnots(), penLags().

---------------------------------------------------------------------
Version 4.2-4

1. gamlss:  
     i) the function lms() has an extra argument method.pk added on. This allows the use of the GAIC method of estimating the     smoothing in pb() parameters. It was found that for large data the local maximum likelihood (which is the default) produces too wiggly centiles. The penalty k is taken from the argument k and it is the same as the one selecting the distribution.

    ii) the function rqres.plot() is fixed and improved to allow worm plot as well as  QQ-plots 
nd 
    iii) The Rsq() function is introduced. It uses the generalised R-squared of Nagelkerke, (1991).

    iv) The term.plot() function has now se=TRUE and  ylim="common" as defaults.

    v) the function gen.likelihood() is introduced.  It creates the log-likelihood of a fitted model. It is used for vcov() and it should be more reliable than previous version of vcov(). 

   vi) The gamlss() function now saves the offset's for all parameters. This is needed for vcov() and gen.likelihood() and allows the vcov() to work with offsets. 	
   
  vii) The wp() function allows now two explanatory variables including factors (see the examples in help). Also it was found that if the model is very bad some of the values  of the worm plot could be Inf so if line=TRUE the function was failing when the cubic fit in lm() was used. Now if there are Inf values it do not fit the lm() model.

  viii) summary() now allows to save the coefficients and standard errors in a table.

    ix) acfResid() is introduced to plot the act function for power function of the residuals, r, r^2, r^3 and r^4.  

    x) the method confint() is introduced for gamlss objects 
   
    xi) attach() was taken out from the functions findhyper(), fitDist(), gamlssML(), histDis(), 
        par.plot(), prof.term() and wp() to comply with the new regulation of R

2. gamlss.tr: 
     i)  A new argument "varying" is introduce in all the truncated functions. It allows the distribution of the response variable to have different truncated points at observational level. (Note that before the truncated parameters had to be the same for all observations)


3. gamlss.dist:
  
     i) NOF distribution has changed to allow for negative values for y. This was suggested by Michael Höhle.

Version 4.2-0

1. gamlss: The functions prof.dev() and prof.term() are improved. 
    The argument step is not anymore compulsory and if not set the argument length is used. 
    For most cases there is no need to have a fine grid  since the function is approximated using splinefun(). 
    Also the output is saved as an "profDeviance.gamlss" object.     
    TO DO: more testing is needed.

2. gamlss.dist: All the FORTRAN routines have their REAL change to "double precession"

3. gamlss.add: 
                i) The function ga() have changed to accept all of Simon Wood's gam() arguments.
                   This allows to fit random Markov fields using gamlss.
               ii) The function penLags() for fitting penalised lag terms and its interface with gamlss la() are added here.
              iii) The functions fitFixBP() and fitFreeKnots() have been moved from the
                    gamlss.util packages to the gamlss.add package to be closer with their interface function fk() which allows 
                    fitting within gamlss 
               iv) BUG: the fk() function needs checking it seems that with the aids data produce the wrong break point


4. gamlss.util: i) The functions fitFixBP() and fitFreeKnots() are moved to package gamlss.add
               ii) garmaFit() for fitting generalised ARMA models is introduced
              iii) centile.ts() for giving centiles for time series data is introduced
               iv) lagPlot() for scatter plotting of lags is introduced.  

5. gamlss : The function fitDist() allows extra arguments "…" to be passed to gamlssML() and gamlss(). 

6. gamlss : The prof.dev() can be used now in conjunction with gamlssML()  

7. gamlss.dist : The distribution function SICHEL has been amended to so in the limited case where sigma is bigger than 10000 and nu > zero it switches to the negative binomial.  (the old version has sigma>1000 which creates problem with the prof.dev() function when was used for nu in the lice data see the Stasinopoulos and Rigby (2007) paper in JSS. Thanks to Ivailo Stoyanov who point out the problem.)


8. gamlss.data : i) the film30 and film90 data sets are introduced 

9. gamlss: The function Q.stats() now allows plotting the resulting matrix for easy identification of the parts of the model which do not fit well. 
                  
Version 4.1-8

1. The cs() and scs() are calling now the R function smooth.spline() rather than the FORTRAN code to comply with R regulations

2. The function vc() is disfuction the user is advised to use the equivalent function pvc()

3. The function lo() is rewritten to comply with R regulation. Now it takes a formula as its first argument rather than a list of explanatory variables. Also no standard errors for the smooth function are provided since the R function loess()  do not provide this information at the moment.

4. The logic link function in the package gamlss.dist is amended so it does not call the R .C function.
  

Version 4.1-7

1. summary.gamlss() now have an argument "save" for saving the output, thanks to  Wilmar Igl

2. gamlssML(): a bug with vcov.gamlssML() function is fixed also "nlminb" is now the default maximisation procedure rather than "optim"

3. The skew Normal 1, SN1, skew Normal 2, SN2,  distributions are introduced in package gamlss.dist().
 
4. The link function "logshiftto2" is added in make.link.gamlss() in package gamlss.dist. The reason for this is to prevent  the degrees of freedom parameter nu in TF2 (see below) to be less than 2.

5. The distributions SST and TF2 are introduced in package gamlss.dist. The distributions are reparametrisations of the ST3 and TF respectively. The sigma parameter in SST and TF2 is the standard deviation of the distribution. Not that the standard deviation is not defined for degrees of freedom less than 2. The "logshiftto2" link function (see above) prevents this.

   
6. The logit normal, LOGITNO and the log normal 2 (with mu as the median), LOGNO2, are introduced in gamlss.dist.

7. The functions gen.Family(), Family(), Family.d(), Family.p(), Family.q() and  Family.p() are introduced in gamlss.dist for generating "log" and "logic" versions of continuous  gamlss.family distributions in the real line.  

8. The generalised Pareto (GP) distribution (a re-parameterisation of PARETO2 and PARETO2o) is introduced in gamlss.dist.

9. A bug in the GB2 and  EGB2 q functions is fixed.
  
10. The default values of the argument cent on lms() and calibrartion() is change to 100*pnorm((-4:4)*2/3) as suggested by Tim Cole. Also a bug which did not allow term.plot() to work with lms() is fixed.

 

 
Version 4.1-5

1. The local function regpen() within pb() has been amended. If the inversion of the smoothing matrix fails within regpen() a QR decomposition is used instead.  (QR is slower that the straight inversion and that why is not the default).

2. The function calibration() to help with centile estimation is added.

3. The function lms() now can be used to produce centile curves when only one explanatory variable exist.

4. Few of the labels on the plots in the package gamlss.demo have been modified     

5. The function hazardFun() and gen.hazard() are  added in the package gamlss.dist so the user can generated a hazard function from any gamlss.family distribution.

version 4.1-2

1. histSmo() is added for density estimation.

2. SHASHo and SHASHo2 and PARETO2o are added to the package gamlss.dist

3. histDist() now has the function gamlssML() as its fitting function.
   gamlss() is used if gamlssNL() failed.
 
4. gamlssML() has now an argument start.from.

5. rDEL() is corrected thanks to Dr. Conrad Burden


version 4.1-1

1. fitDist(), the normal distribution NO(), was added to the list of .realline so it appears in the    AIC list

2. the following distributions are added to package gamlss.dist: YULE, WARING, GEOM, IGAMMA, PARETO2

version 4.1.0

1, BCTo, BCPEo, and BBCGo are added so BCt, BCPE and BCCG can has mu.link="log" as a default 

2. gamlssML has now method summary()

3. vcov() a bugs is corrected thanks to Tom Jagger

4. package gamlss.util: the functions fitFixBP() and fitFreeKnots() for fitting fixed
         and free  break points respectively have been improved

5. the function random is modified to allow Local maximum likelihood estimation of the smoothing parameter lambda 

6. pvc() : has been modified to allow fixing the dfs when the "by" argument is a factor (Tim Cole suggest it).

7. predict.gamlss(): now works with offsets

8. wp(): now works with any fitted object which has the method resid()

9. dop() is renamed dtop() and now works with any fitted object which has the method resid()


10. fitted.plot() is renamed fittedPlot() to avoid S3 problems 

11. pvc(): now predict is working when the argument  "by" is a continuous variable, thanks to Torsten Hothorn for point out to us.
   

12. fitDist() is introduced. This function fits several parametric distributions to a vector of data and chooses the one with minimum GAIC.    

version 4.0.3

1. stepGAIC.VR(): has been modified so if one or more models
     failed to fit the function still continue with the
     step wise algorithm.
2. stepGAICAll() is renamed now stepGAICAll.A() for strategy A.
      Other strategies will follow.
3. update.gamlss() now the argument 'what' takes also the value
   "All". Note that 'what' is relevant only if the argument
   'formula.' is set.

4. stepGAICAll.B() is introduced. Strategy B fits the relevant
        term in all the distribution parameters.

5. centiles.fan() is introduce for fan-chart of the centiles.

6. centiles() has an extra option "points" on whether to
         include the points in the plot or not

7. the package gamlss.data now includes a help file for the
          data.frame acidity. 

version 4.0.0

1. pb(): if set the degrees of freedom are allowed to be below 1

2. BEINF0 and BEINF1 are added in gamlss.dist

3. gamlss() is amended to use the same environment when it
     changes the formula in "mcall". This is to correct a problem
     when gamlss is called from other functions (reported by Dr
     Willem Vervoort)

4. stepGAICAll() is introduced in the gamlss package for
      the selection of terms in all the parameters

5. add1.gamlss() and drop1.gamlss() are added to complement
       addterm.gamlss() and dropterm.gamlss() (which required
       MASS)

6. LR.test(): The likelihood ratio test function for nested GAMLSS
           models is introduced.

7. PO, NBI, NBII, PIG, ZIP, ZIP2m DEL are retested and
   modified

8. gamlss.tr: the definition of right truncation in discrete
     distributions has changed. For example right truncation
     at 14, excludes 14 as possible value.

9. stepTGD() is introduce for selection of terms using a test
             data set.

10. centiles.com() "main" is added as argument


11. gamlssNP() the function has being modified by Michael H?hle
    to correct  for situations where the likelihood calculation
    produced NA's.

12. In some of the examples used in the help files of gamlss
    the function cs() has change to pb() to emphasize the fact
    that the degrees of freedom for the smoother are now
    estimated.

13. fitted.plot() now takes xlab argument.

14. histDist() has an extra data argument now.

15. vcov.gamlss() it will give a warning if the gamlss call contains
    $ (because the function inteprFormula.default() in gamlssNonLinear() does not
    accept $'s in formulae) resulting in the failure of the exact standard errors
    obtained from the vcov() function.

16. pvc() is introduced for fitting penalised beta splines
    varying coefficient models

17. all the functions in all the package gamlss have been
    modified to take 3-parameter or  4-parameter binomial type
    response variable.

18. term.plot() now has "ylim" argument (thanks to Christian
     Kiffner)

---------------------------------------------------------------------
version 3.1.0

1. cy(): a bug is corrected

2. the packages gamlss.util is added. It has function useful
   for a GAMLSS course

3. the package gamlss.add is added. It contains new additive
   terms. Some of the functions are experimental and not tested
   rigourously.

4. new data are added to package gamlss.data

5. a bug is fixed in package gamlss.tr (related to the change
   of the first argument of the d function of the distributions
   from y to x)


---------------------------------------------------------------------
version 3.0.1

1. cy() has been modified. Now the B-splines starts from min(x)
    to max(x). It also includes an option for fitting seasonal
    effects in Time Series.

2. pb(): the option quantiles has been modified, when is TRUE
         the B-spline base is created using the function
         splineDesign().

3. IMPORTANT: All the probability "d" functions in the
   gamlss.family distribution had the fist argument y replaced
   with x to be consistent with other distribution function in R.
   This had a knock off effect in the other gamlss packages.



---------------------------------------------------------------------
version 3.0.0

The main feature of this version is the separation of the
distributions and the data from the gamlss package. To do that
the dependencies between the packages have changed. In the new
arrangement gamlss.dist and gamlss.data can be loaded
separately from the package gamlss. Loading the package gamlss
automatically will load gamlss.dist and gamlss.data.

The zero adjusted gamma (ZAGA) is added to the distributions

version 2.0.3

1. additive.fit: has been modify to allow multiple lambda.
   This should not effect old additive functions with single lambda


version 2.0.0

1. find.hyper(): is corrected from version 1.9.0

2. histDist(): "main" is added on the discrete binomial case

3. ridge() and ri() are introduced as additive term functions
   to perform ridge regression

4. pb() is introduced as additive term. The pb() is similar but
    faster than the ps() function.

5. cy() is is introduced as additive term together with its
   time series version cyts()

6. nlgamlss() and gamlssNonLinear() have changed to accommodate binomial responses (thanks to Laura Hatfield)

7. lpred() is fixed to give s.e. if the the response is Surv(). (thanks to Albert Wong)

8. WEI, WEI2, WEI3: names of variables defined at the initial
  value had been changed so they do not clash with users defined
  names. (thanks to Rob Musk)


version 1.9.0

1. prof.dev and prof.term have been modified to avoid the problem of creating two pages in pdf files

2. predict.gamlss() has been modified

3. predictAll() has been created as a single call for prediction on a gamlss object

4. distributions: BB BCCG BCPE  BCT BCTuntr BE BEINF BI GA GU IG
         JSU JSUo LNO LO LOGNO NO NO2 NET? NBI NBII PE PE2 RG TF
         SI SEP ZIP ZAIG  have been modify to pass checkUsage()
         [Also all the distributions in package gamlss.dist].

5.   function: find.hyper() has been modified

6. make.link.gamlss() has been modified: logshift has change to logshiftto1
         while logitshifted has been taken off. The function
         make.link.gamlss() now allows the creation of a function
         defining all "own" link functions.

7. The functions VGD1, VGD2, and TGD are added.

8. BEo is added to the gamlss.family

9. histDist() : the argument freq is now corrected

10. cs() is changed to give the the correct SE's according to
          the the new version of smooth.spline

11. s() is introduced which does the same job as cs()

12. predict.gamlss() and lpred() are modified to avoid a warning
---------------------------------------------------------------------
version 1.8.0

1. "\non_function{}" is eliminated for the help files

2. ST3: cross derivatives are implemented

3. histDist(): is changed so "..." passes only to gamlss(),
              also has different arguments from the previous
              version.
4. PO() : mu.inital is changed

5. SEP.rd : is changed

6. NBI(), BB(), BEINF(): d2ldmdd is changed

7. rqres() : has now an extra argument censored=NULL to accommodate discrete censored data

8. NET, RGE, IG: small changes to comply with cens()

8. GA, NO, BCCG, BCPE, BCT, GU, RG, TF and ZAIG: the initial values for mu change to (y-mean(y))/2

9. A bug in dTF is fixed

10. PE has been modify to cope with nu go to large values

11. the glim.fit() function in gamlss() had few new stops to prevent fitting when wv or wt goes has NA's or Inf's

12 The following new distributions have been added to gamlss and gamlss.dist packages
    Power exponential type 2 (PE2)
    Skew t type 1 (ST1)
    Skew t type 2 (ST2)
    Skew t type 3 (ST3)
    Skew t type 4 (ST4)
    Skew y type 5 (ST5) the old ST3
    Skew Exponential Power type 1 (SEP1)
    Skew Exponential Power type 2 (SEP2)
    Skew Exponential Power type 3 (SEP3) old SEP
    Skew Exponential Power type 4 (SEP4)
    Generalized t (GT)
    Generalized beta type 1 (GB1)
    Generalized beta type 2 (GB2)
    Exponential generalized beta type 2 (EGB2)
 13 All the old continuous distributions have been re-checked
    using the function testContDist() and as a consequence some
    alterations have been made in some of them to improved their
    fitting performance. The user should find that fitting continuous
    distributions now is lot more robust.
  14 NO.var is renamed NO2
  15 centiles() and centiles.split(have been modified after suggestions from Steve Ellison)

---------------------------------------------------------------------
version 1.7.0

1. VGA(): The function has been modified

2. The glim.control trace has been changed to glm.trace

3. histDist : modified to accept gamlss arguments (as method) for discrete data

4. All the url have changed to www.gamlss.com

5. gamlss.bi.list became global .gamlss.bi.list

6. the residuals.gamlss() functions has changed:
           For the option "z-scores"  if weights have been used it takes the
           following action:
           if weights are integers (possibly 0,1) it assumes that frequencies are used
           and expands the residuals using "rep(obj\$residuals,w)
           if weights are not integers it does not take any action.
           As a consequence  the length of the residuals obtain from resid(obj) is not
           necessarily equal to the length(fitted(obj))
           This is done do the plot(), wp() and rqres.plot() functions working properly with
           weighted (using frequencies) analysis.
7. plot(): has been modified to cope with the new resid(obj) function

8. rqres.plot() :  has been modified to cope with the new resid(obj) function

9. wp(): has been modified to cope with the new resid(obj) function

10. summary() : a bug in summary has been fixed

11. The SEP and SHASH distributions have the cross derivatives added plus a bug in the q function fixed

---------------------------------------------------------------------
version 1.6.0

1. PIG : now PIG is working with CG() method. (Monday, January 8, 2007)

2. gamlss(): all references to length(y) have been removed to make the function
             able to cope with response variables that they are not vectors.

3. gamlss(): the interpretation of Surv() as response variable have changed.

4. the vcov() function has changed for names to appear (Tuesday, March 13, 2007)

5. the summary() function has changed, now the default se are calculated from the
    vcov matrix rather than the qr decomposition

6. centiles() has been modified to cope with y-variable matrix (i.e. Surv())

7. predict.gamlss() has change to work with truncated distributions and censored data
#-------------------------------------------------------------------
version 1.5.0

1. vcov : for gamlss is back having taken out in version 1.4.0

2. gamlss.control(): The argument save is introduced.
     save=TRUE saves on exit everything as al versions of gamlss.
     save=FALSE saves only limited information. This is useful
     when gamlss() is called repetitively with a large set of data.

3. VGD : a bug in the function is corrected

4. centile.pred : a bug is corrected (Friday, December 1, 2006 )

5. gamlss(): now both RS(), CG() and mixed() methods saved objects
    have the same elements (Tim Cole pointed out the discrepancies)


---------------------------------------------------------------------
version 1.4.0

1. The make-link-gamlss has change. Now save a link-gamlss object.

2. gamlss: the formals() used within gamlss has changed to conform with the R-2.4.0
   version
---------------------------------------------------------------------

version 1.3-0

1. gamlss : mu.fix, sigma.fix, nu.fix, tau.fix are checked now if they are logical
   variables

2. centiles.pred : the code haves change to allow prediction with models with
   fixed parameters i.e. nu.fix=TRUE

3. the logLik method for gamlss is added.

4. BI and BB are modified


---------------------------------------------------------------------
version 1.2-0

1. centiles, centiles(), centiles.split(), centiles.com(), centiles.pred():
   the restriction that it should work only with continuous variable is taken off
   (the binomial case is not working yet).

2. the rqres() function is added and also is included in gamlss() in case
   trun() is called without generating first the d and p functons.

3. All distributions have the rqres() function added
   in their residuals calculation

4. NBI(), NBII() the limit to the PO() when sigma goes to zero is added to the
   d and p functions rather in the fitting function. Also in the BCT and
   TF distributions if degrees of freedom go to infinity the d function goes to BCCG
   and NO respectively

5. rqres.plot(): the restriction that it applies only to "Discrete" families is
   taken off. This allow the possibility to be used with "Mixed" families.
   For "Continuous" it will reproduce the same qqplot.

6. The non-linear add-on package gamlss.nl is created

7. The add on package gamlss.tr for truncated distributions is created

8. vcov() is introduce for gamlss objects but is not appropriate for all
   additive models. vcov(, type="se") can be used for more accurate s.e.'s

9. centiles.pred(): the function calc.cent() within  centiles.pred()
        has been changed by by Huiqi Pan.

10. predict():  type="response" it did not pick up the right function if
                the link was set i.e. IG(mu.link=identity).
                This is corrected now.

11. lpred(): the same error as above if se=T was set.

---------------------------------------------------------------------

version 1.1-0

1. A small amendment in GLIM.fit (within gamlss()). It could effect
     early iterations in the RS() algorithm.

2. NBI and NBII: if sigma goes to zero it picks up the Poisson deviance.

3. NBII: the observed second derivatives for mu and sigma (which do not always work)
   are replaced if needed by the squares of the first derivative

4. own links are introduced. Documentation can be found in the help file
   for make.link.gamlss.

5. BI "own" has been added to the options of mu.link.
   For example BI(mu.link="own") is allowed now.

6. The stepAIC() function is now renamed as stepGAIC.VR().
   The old stepGAIC() is now renamed as stepGAIC.CH().
   The new function stepGAIC() has an new argument
   "additive", which determines  whether stepGAIC.CH()
   (with additive=TRUE) or stepGAIC.VR()
   (with additive=FALSE) is used.

7. par.plot(), prof.term(): detach() is added in the end of
   the functions.

8. TF: In the calculation of G.dev.incr, if nu > 1000000, now it
       picks up the normal distribution deviance

9. BCT:  In the calculation of G.dev.incr, if tau > 1000000 it picks
         up the BCCG distribution

10. a bug is corrected in Q.stats

11. bug corrected in pp

12. the delta step in the numerical derivative within the SI have
    changed to 0.0001

13. qPIG, qSI qBB: have been corrected

---------------------------------------------------------------------
Older versions

version 1.0-0

This was the fist version in CRAN.

version 0.5-0

1. The weights argument of the gamlss() function has being redefined to be able to fit data with
   identical cases (that is, data with frequencies).
2. The code in wp() is cleared after some suggestion from Elaine Borghie of WHO


Version 0.4-4

1. The RS() and CG() algorithms have changed to accommodate automatic steps in the parameters search
   The gamlss.control() function have now a new argument 'autostep=TRUE'

2. Several bugs related to BEING distribution are corrected The most important one is that now
   smooth functions can be fitted with BEINF. This is a quick fix, more permanent solution will be in the
   future


Version 0.4-3

1. The PIG and Sichel distribution are now in Both distributions need FORTRAN code


Version 0.4-2

1. The steps in glim() function used in the method RS() have changed so it can be used with both
   linear and non linear smoothing functions


Version 0.4-1

1. The beta distribution, BE(), the beta inflated, BEING(), and the Zero inflated poisson, ZIP(), are in.


Version 0.4-0

1. The gamlss() function has been updated so it can easily extended to more that four distribution parameters


Version 0.3-9

1. fitted() coef() and deviance() have been modified so their argument take only characters
   i.e what="sigma" not what=sigma
2. fv() is created to be similar to fitted() but can take what=mu



Version 0.3-8

1. profile.R a bug has been corrected
2. gamlss.sm.list() it taken out of gamlss() and renamed .gamlss.sm.list()
3. The gamlss output now saves xlevels
4. The gamlss output now save terms for all parameters
5. predict.gamlss() is created
   lpred() is the function for old data
   predict(() can be used for both old and new data
6. term.plot() is created
7. centiles.pred() is created



Version 0.3-7

Changes from 03-6

1. gamlss() : have change so the to save the penalty (if penalized likelihood is used).
             Now obj$pen contains the sum of the penalties in all parameters
             and obj$mu.pen, obj$sigma.pen, obj$nu.pen obj$tau.pen save the penalties is smoothing is occurred
2. deviance.gamlss() has anew option what="G" for Global and "E" foe extended deviance.
3. cs() the option c.spar accepts now vectors or list




Version 03-5



1. fitted.plot() has been change to include multiple models
2. the method AIC for gamlss has been included
3. centiles.com() : for comparing centiles for more than one model
4. qtest(): have changed in allowing 2 rather than 3 df's more than the maximum df
5. gamlss(): new argument start.from is introduced




Version 03-4

Changes from 03-3

1 The find.df() had been renamed as find.hyper() and is modified to accept arguments for optim()
2. The prof.dev() and prof.term() have been added extra
   argument for saving the resulting matrix
   plot=TRUE
   perc= 95
3. the histDist() function is introduced
4. the par.plot() function is introduced
5. the control parameter for the additive.fit are now in glim.control()
6. the "fitter" vector created by gamlss() in the workspace environment now is deleted on exit from gamlss()
7. the na.action in gamlss is activated this would help if NAs are in the data.frame
8. the lp() function extracting the linear predictors is introduced
9. the plot() function now prints the right sign in the coefficient of skewness
10. the plot() function has been modified so to accept na.exclude in the argument na.action in gamlss
