L-Ippel/SEMA: Streaming Expectation Maximization Approximation

The SEMA algorithm fits multilevel models in the context of streaming data (a.k.a. data streams). Using online learning, the model is updated a data point at the time. SEMA algorithm only stores summarizing statistics, which limits memory usage, and because it never returns to the data, sema algorithm is computationally efficient. The current state of the algorithm is able to fit multilevel models with fixed and random effects.

Getting started

Package details

MaintainerThe package maintainer <[email protected]>
LicenseGPL-2 | GPL-3
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
L-Ippel/SEMA documentation built on Aug. 11, 2018, 10:12 p.m.