The new features in version 2.1-2 are as follows:
package gamlss:
- The function histSmo() is added for density estimation.
- The function histDist() now has the function gamlssML() as its main fitting function. The fitting function gamlss() is only used if gamlssML() fails.
- The function gamlssML() has now an argument start.from.
- In the function fitDist(), the normal distribution NO() is added to the list of “.realline” so it also appears in the AIC list
- The function gamlssML() has now method summary()
- A bugs is corrected in the function package vcov(), thanks to Tom Jagger
- The function random() is modified to allow Local maximum likelihood estimation of the smoothing parameter lambda
- The function pvc() has been modified to allow fixing the dfs when the “by” argument is a factor (Tim Cole suggest it).
- The predict.gamlss() function works now with offsets
- The worm plot function wp() works now with any fitted object which has the method resid()
- The function dop() is renamed as dtop() and now works with any fitted object which has the the method resid()
- The function fitted.plot() is renamed fittedPlot() to avoid S3 problems
- pvc(): now predict is working when the argument “by” is a continuous variable, thanks to Torsten Hothorn for point out to us. The fitted function fitDist() is introduced for fitting different distributions to a single set of data. This function fits several parametric distributions to a vector of data and chooses the one with minimum GAIC.
package gamlss.dist:
- The gamlss.family distributions SHASHo and SHASHo2 and PARETO2o are added to the package gamlss.dist
- The random generating function rDEL() is now corrected thanks to Dr. Conrad Burden
- The following gamlss.family distributions are added to package gamlss.dist: YULE, WARING, GEOM, IGAMMA, PARETO2
- The gamlss.family distributions BCTo, BCPEo, and BBCGo are added so BCT, BCPE and BCCG can have mu.link=”log” as a default
package gamlss.util:
- The functions fitFixBP() and fitFreeKnots() for fitting fixed and free break points respectively have been improved
