News for Package 'DirichletReg'

Changes in Version 0.5-1:

   Fixed a bug when using the 'subset' argument.

   Added tolerance for normalization check to 'DR_data'.

   moved 'NEWS' to the new fancy 'NEWS.Rd' file/format.

   Added the possibility to do quick analyses and transforming data
    "on the fly", like 'DirichReg(DR_data(A) ~ 1)'.  However this is
    only intended for quick checking purposes and may be removed at any
    time.

Changes in Version 0.5-0:

   Transformation in 'DR_data' is now not only 'TRUE'/'FALSE', but, by
    default, a small numeric value to avoid troubles with floating
    point numbers close to 0 or 1.

   Time-critical routines were implemented in 'C' (pure R versions are
    available, see '?ddirichlet').

   'anova.DirichletRegModel' now invisibly returns results as an
    object that is printed by a method.

   Optimized estimation routines.

   Fixed a bug in the predict method.

   Started development of a comprehensive test-suite using 'testthat'.

   Published a working paper on the package:
    Maier, M. J. (2014). DirichletReg: Dirichlet Regression for
    Compositional Data in R. Research Report Series / Department of
    Statistics and Mathematics, 125. WU Vienna University of Economics
    and Business, Vienna. <URL: http://epub.wu.ac.at/4077/>

   Added vignette with code to the working paper.

   Added citation info.

Changes in Version 0.4-1:

   The 'trafo' Argument of 'DR_data' has been changed, because it has
    lead to problems in practical applications when numbers very close
    to 0 or 1 were present.

   'DR_data' checks for negative values and generates an appropriate
    error message.

   'DR_data' has been made more robust in the presence of 'NA's.

Changes in Version 0.4-0:

   Data structure generated by 'DR_data' has changed  the new objects
    can now be integrated into data frames.

   Formula processing is now handled by the package 'Formula'.

   New methods have been implemented, especially for the class
    'DirichletRegModel'.

   The documentation is now quite complete.

   Some speed improvements could be achieved.

   Lots of minor (invisible) changes.

Changes in Version 0.002:

   Added the analytical Gradient and Hessian for both
    parametrizations.

   Optimization: preliminary results by BFGS that become starting
    values for Newton-Raphson optimization computing the final results.

   Implemented some residuals

   Updated help entries

