I am looking for some libraries in R that can do incremental learning (also called online or sequential learning). The use case of such learning in comparison to traditional batch methods would be to process large amounts of data. Such practices include streams and data from sensors, where it is not feasible to use always the same model or to rebuild the model from scratch every time. Any machine learning algorithm that can use only single new example to change the model would suffice. However, the model itself must not hold on to old data (as you can imagine it would soon get too big), instead just calculating some statistics about data.
For multivariate regression, online approach like Stochastic gradient descent would be a good option. For regression / model trees something like this article comes to mind. I am looking for such library where relatively good prediction accuracy (with respect to traditional batch methods) could be achieved based on the evolving model.