Changes in version 6.1-8                        

  - The LOGSHASH is introduced as a distribution in the range (0, Inf).
    It could be fitted before but it had to be generated by the function
    gen.Family, i.e. gen.Family("SHASH", "log").

  - The LOGSHASHo is also introduced as a distribution in the range (0,
    Inf) but at moment remains hidden.

                        Changes in version 6.1-7                        

  - the plotting functions in the file PlottingDistributions.R i.e
    count_1_23 e.t.c. were checked.

                        Changes in version 6.1-4                        

  - The dPO, pPO and qPO are updated so the length of y is equal to the
    length of mu

  - the functions test_continuous_gamlss_dist() and
    test_discrete_gamlss_dist() are added to the package for checking
    distributions but the functions not have help files.

                        Changes in version 6.1-3                        

  - BCT the BCPE and the BCCG have new d, p and q functions

  - the q functions for all distributions are updated so the limits are
    defined properly for example for the BEINF we have;
    
    -- q[abs(p-0)<1e-15] <- 0
    
    -- q[abs(p-1)<1e-15] <- Inf
    
    -- q[p < 0] <- NaN
    
    -- q[p > 1] <- NaN.

                        Changes in version 6.1-2                        

  - The GitHub repository is now hosted under the new gamlss-dev
    organization: https://github.com/gamlss-dev/gamlss/.

                 Changes in version 6.1-1 (2023-08-23)                  

  - The package is now hosted on GitHub at
    https://github.com/mstasinopoulos/GAMLSS-Distibutions/.

  - Add an S3 class GAMLSS and corresponding methods encompassing all
    distributions from the gamlss.dist package using the workflow from
    the distributions3 package (contributed by Achim Zeileis). The idea
    is that from fitted gamlss model objects predicted probability
    distributions can be obtained for which moments (mean, variance,
    etc.), probabilities, quantiles, etc. can be obtained with
    corresponding generic functions. See useR! 2022 presentation by
    Zeileis, Lang, and Hayes for an overview of the distributions3
    package.