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.
You can also run the demos for the project. Run:
algorithmentry with the name of the algorithm (i.e., the class name), and could have
algorithm_paramswhich is a list of hyperparameters for the specific algorithm. Furthermore, it could have a
paramsentry with two entries:
nbinsspecifying the number of bins to use for the discretization step, and
onlinea boolean specifying whether the algorithm should be treated as an online one.
You can specify interventions as follows:
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/