hermiter: R package for Sequential Nonparametric Estimation

TL;DR

The paper presents the hermiter R package that provides efficient, sequential, and parallelizable nonparametric estimators for univariate and bivariate distributions using Hermite series, suitable for large and streaming data.

Contribution

It introduces a new R package implementing Hermite series based estimators for distribution functions and correlation, optimized for sequential, large-scale, and distributed data analysis.

Findings

Enables efficient sequential estimation of distributions and correlations.

Supports parallel and distributed computation with mergeable estimators.

Applicable to large and streaming data scenarios.

Abstract

This article introduces the R package hermiter which facilitates estimation of univariate and bivariate probability density functions and cumulative distribution functions along with full quantile functions (univariate) and nonparametric correlation coefficients (bivariate) using Hermite series based estimators. The algorithms implemented in the hermiter package are particularly useful in the sequential setting (both stationary and non-stationary) and one-pass batch estimation setting for large data sets. In addition, the Hermite series based estimators are approximately mergeable allowing parallel and distributed estimation.