Re-derived gradients/Hessians, optimized C-code, and tweaked computation of starting values which leads to considerably better performance.
Fixed some bugs in
Expanded testthat checks.
All likelihood and gradient functions are now written in C (all called via
.Call() now instead of
.C()) which leads to a considerable gain in speed.
drop1 method for Dirichlet regression models was added.
As it is still experimental and will probably change, use it with care.
Options such as
scope will be added in one of the next releases.
Fixed a bug in the
Expanded the testthat check- and test-suite.
Known issue: If you have collinear (aliased) terms, the estimation will fail. This will be handled automatically in subsequent releases, but for now, please remove the respective terms. If you fit a model and it says something like:
Error in prepareFixed(start = start, activePar = NULL, fixed = fixed) : At least one parameter must not be fixed using argument 'fixed'you most likely have collinear terms or “empty” combinations of interaction terms.
Fixed checking functions in
Fixed a bug when using the
Added tolerance for normalization check to
NEWS to the new fancy
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.
DR_data is now not only
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
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. http://epub.wu.ac.at/4077/
Added vignette with code to the working paper.
Added citation info.
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
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
The documentation is now quite complete.
Some speed improvements could be achieved.
Lots of minor (invisible) changes.
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