OnlineSuperLearner: SuperLearner with online functionality for time-series analysis

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.

Features

• Automatic optimal predictor ensembling via sequential cross-validatio.
• Can be extended with several algorithms.
• Has several pre-defined summary measures

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("frbl/OnlineSuperLearner")

Rebuilding the documentation and webpage:

# Generate documentation
devtools::document()

# Build vignettes
devtools::build_vignettes()

# Rebuild webpage
pkgdown::build_site()

Examples

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:

demo('cpp-demo', package = 'OnlineSuperLearner')

The algorithm syntax

• Each entry should have an 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.

algos <- append(algos, list(list(algorithm = 'ML.SVM',
                        algorithm_params = list(),
                        params = list(nbins = nbins, online = FALSE))))

The intervention syntax

You can specify interventions as follows:

intervention <- list(variable = 'A', when = c(2), what = c(1))

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).

TODO

• View the issues page

References

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/