stockAggregators: Stock aggregators
In boostr: A modular framework to bag or boost any estimation procedure.

Description Usage Arguments Details Value Note See Also

Parallelized implementations of weighted and unweighted "classification by voting" procedures.

arcx4Aggregator(estimators, ..., .parallelPredict = FALSE,
  .parallelTally = FALSE, .rngSeed = 1234)

vanillaAggregator(estimators, ..., .parallelPredict = FALSE,
  .parallelTally = FALSE, .rngSeed = 1234)

weightedAggregator(estimators, weights, ..., .parallelPredict = FALSE,
  .parallelTally = FALSE, .rngSeed = 1234)

`weights`	a vector of scalar weights associated to each estimator in `estimators`
`estimators`	a list of estimators which must produce output in the same response-space. This is usually the output of some reweighter function.
`...`	this does nothing – meant to swallow auxillary output from reweighter function.
`.parallelPredict`	a boolean indicating if prediction should be carried out in parallel.
`.parallelTally`	a boolean indicating if vote tallying should be performed in parallel. Unless you have more than 1,000 votes / observation, you probably won't see much performance gain by parallelizing this step.
`.rngSeed`	the RNG seed sent to `predictClassFromVote` or `predictClassFromWeightedVote`. in the case of a tie.

arcx4Aggregator is just vanillaAggregator by another name.

If performing regression and your estimators produce NA's, you can have weighted.mean remove the NA's by passing na.rm=TRUE to weightedAggregator's function call.

a function whose sole argument is newdata and whose output is the aggregated predictions of the boosted ensemble, estimators.

For internal bookkeeping, this function is inherits from the 'aggregator' class.

It's assumed that the estimators in estimators are classifiers. More aptly, their output is either of factor or character-type.

predictClassFromWeightedVote; predictClassFromVote

Other aggregators: adaboostAggregator; arcfsAggregator; boost, boost.function, boost.list