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.

The 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.



  • 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.


  • 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.


  • mclogit.fit and mclogit.fit.rePQL are exported to enable their use by other packages.