This is the current version of the Online SuperLearner for Time-Series data R package. Note that this version is in active development, and considered to be pre-alpha software. Be very careful interpretting any results from this package.
For an example on how to run the OnlineSuperLearner, view the Jupyter notebook, or the R/OnlineSuperLearner.Simulation.R
file. For a complete guide see the documentation.
You can also run the demos for the project. Run:
algorithm
entry with the name of the algorithm (i.e., the class name), and could have algorithm_params
which is a list of hyperparameters for the specific algorithm. Furthermore, it could have a params
entry with two entries: nbins
specifying the number of bins to use for the discretization step, and online
a boolean specifying whether the algorithm should be treated as an online one.You can specify interventions as follows:
where variable
is the variable to perform the intervention on, when
is when the intervention should take place (at t= 2
in this example) and what
what the intervention should be (1 in this case, but this could e.g. also be 0).
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
van der Laan, M. J., & Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media. http://www.targetedlearningbook.com
Benkeser, D., Ju, C., Lendle, S. D., & van der Laan, M. J. (2016). Online Cross-Validation-Based Ensemble Learning. U.C. Berkeley Division of Biostatistics Working Paper Series, Paper 355. http://biostats.bepress.com/ucbbiostat/paper355/