Few days ago a new version of DALEX was accepted by CRAN (v 0.4.9). Here you will find short overview what was added/changed.

DALEX is an R package with methods for visual explanation and exploration of predictive models.
Here you will find short overview with examples based on Titanic data.
For real world use cases:
Here you will find a conference talk related to credit scoring based on FICO data.
Here you will find an example use case for insurance pricing.

Major changes in the last version

Verbose model wrapping

Function explain() is now more verbose. During the model wrapping it checks the consistency of arguments. This works as unit tests for a model. This way most common problems with model exploration can be identified early during the exploration.

Support for new families of models

We have added more convenient support for gbm models. The ntrees argument for predict_function is guessed from the model structure.
Support for mlr, scikit-learn, h2o and mljar was moved to DALEXtra in order to limit number of dependencies.

Integration with other packages

DALEX has now better integration with the auditor package. DALEX explainers can be used with any function form the auditor package. So, now you can easily create an ROC plot, LIFT chart or perform analysis of residuals. This way we have access to a large number of loss functions.

Richer explainers

Explainers have now new elements. Explainers store information about packages that were used for model development along with their versions.
Latest version of explainers stores also sampling weights for the data argument.

A bit of philosophy

Cross-comparisons of models is tricky because predictive models may have very different structures and interfaces. DALEX is based on an idea of an universal adapter that can transform any model into a wrapper with unified interface that can be digest by any model agnostic tools.

In this medium article you will find a longer overview of this philosophy.