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In multivariate nonparametric analysis, sparseness of the covariates alsocalled curse of dimensionality, forces one to use large smoothing parameters.This leads to a biased smoother. Instead of focusing on optimally selecting thesmoothing parameter, we fix it to some reasonably large value to ensure anover-smoothing of the data. The resulting base smoother has a small variancebut a substantial bias. In this paper, we propose an R package named ibr toiteratively correct the initial bias of the (base) estimator by an estimate ofthe bias obtained by smoothing the residuals. After a brief description ofIterated Bias Reduction smoothers, we examine the base smoothers implemented inthe packages: Nadaraya-Watson kernel smoothers and thin plate splinessmoothers. Then, we explain the stopping rules available in the package andtheir implementation. Finally we illustrate the package on two examples: a toyexample in RxR and the original Los Angeles ozone dataset.

Iterative bias reduction multivariate smoothing in R: The ibr package