The new version of gamlss is 4.2-0. The following are the changes made:

**package 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 instead. For most cases there is no need to have a fine grid since the function is approximated using*splinefun()*. The output is saved as an “profDeviance.gamlss” object. Note that more testing is needed for the reliability of the function. - The function
*fitDist()*allows extra arguments “…” to be passed to*gamlssML()*and*gamlss()*. - The
*prof.dev()*can be used now in conjunction with*gamlssML()* - The function
*Q.stats()*now allows plotting the resulting matrix for easy identification of the parts of the model which do not fit well. - The
*cs()*and*scs()*are calling now the R function*smooth.spline()*rather than the FORTRAN code to comply with R regulations - The function
*vc()*is disfuction the user is advised to use the equivalent function*pvc()* - 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. - The logic link function in the package gamlss.dist is amended so it does not call the R .C function.
*summary.gamlss()*now have an argument “save” for saving the output, thanks to Wilmar Igl*gamlssML()*: a bug with*vcov.gamlssML()*function is fixed also “nlminb” is now the default maximisation procedure rather than “optim”- 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.

**package gamlss.dist**

- All the FORTRAN routines have their REAL change to “double precession”
- 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.)
- The skew Normal 1, SN1, skew Normal 2, SN2, distributions are introduced
- 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.
- 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.
- The logit normal, LOGITNO and the log normal 2 (with mu as the median), LOGNO2, are introduced in gamlss.dist.
- 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.
- The generalised Pareto (GP) distribution (a re-parameterisation of PARETO2 and PARETO2o) is introduced in gamlss.dist
- A bug in the GB2 and EGB2 q functions is fixed.

**package gamlss.add**

- The function
*ga()*have changed to accept all of Simon Wood’s gam() arguments. This allows to fit random Markov fields using gamlss. - The function
*penLags()*for fitting penalised lag terms and its interface with gamlss*la()*are added here. - 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

**package gamlss.data**

- the
*film30*and*film90*data sets are introduced

**package gamlss.util**

- The functions
*fitFixBP()*and*fitFreeKnots()*are moved to package gamlss.add *garmaFit()*for fitting generalised ARMA models is introduced*centile.ts()*for giving centiles for time series data is introduced*lagPlot()*for scatter plotting of lags is introduced.