=============================== mclogit 0.4 published on CRAN =============================== .. post:: 2016-12-26 :category: software, mclogit .. previewimage:: mclogit-mtable.png A new version 0.4 of package `mclogit `__ has been published on `CRAN `__, which adds a few new features to the previously published version. One highlight of the new release is improved support for multinomial baseline logit models, i.e. models for categorical dependent variables with unordered categories (a.k.a. nominal-level dependent variables) with help of the function ``mblogit()``. Such models could already be fitted with ``mclogit()``, yet this required some additional work in data-management and model construction. The new ``mblogit()`` function allows to fit models with categorical dependent variables right away. Its output will be numerically identical with the output of ``multinom`` from the package `nnet `__, but the results are reported in more “conventional” way similar to to the results of ``glm()`` and the like. At present, there is no support for random effects in multinomial baseline logit models. These are going to be added in the next release (0.5), along with random slopes in multinomial conditional logit models. Changes since 0.3 ~~~~~~~~~~~~~~~~~ New features ^^^^^^^^^^^^ - New function ``mblogit`` to fit multinomial baseline logit models. - New ``nobs`` and ``extractAIC`` methods for ``mclogit`` objects, so that ``drop1.default`` should work with these. - ``mclogit`` and ``mclogit.fit`` now allow user-provided starting values. Bugfixes ^^^^^^^^ - ``getSummary`` methods now return “contrasts” and “xlevels” components. - Fixed prediction method for ``mclogit`` results. - Corrected handling of weights and standard errors of prediction. - Matrices returned by the ``mclogit`` method of ``vcov()`` have row and column names. User-visible changes ^^^^^^^^^^^^^^^^^^^^ - ``mclogit.fit`` and ``mclogit.fit.rePQL`` are exported to enable their use by other packages.