  We propose a method for obtaining maximum likelihood estimates in a model
with continuous and binary outcomes. Combinations of left and right censored
observations are also naturally modeled in this framework. The model and
estimation procedure has been implemented in the R package lava.tobit.
  The method is demonstrated on brain imaging and personality data where
measurement error on predictor variables is handled in a latent variable
framework. A simulation study is conducted comparing the small sample
properties of the MLE with a limited information estimator.
