hermite_estimator: A class to sequentially estimate univariate and bivariate...

Description Usage Arguments Value Author(s) Examples

View source: R/hermite_estimator.R

Description

The hermite_estimator class provides a unified interface to the univariate and bivariate Hermite series based estimators, leveraging generic methods and multiple dispatch. Methods are included for the sequential or one-pass batch estimation of the full probability density function and cumulative distribution function in the univariate and bivariate settings. Sequential or one-pass batch estimation methods are also provided for the full quantile function in the univariate setting and the Spearman's rank correlation estimator in the bivariate setting.

Usage

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hermite_estimator(
  N = 30,
  standardize = TRUE,
  exp_weight_lambda = NA,
  est_type = "univariate"
)

Arguments

N

An integer between 0 and 75. The Hermite series based estimator is truncated at N+1 terms.

standardize

A boolean value. Determines whether the observations are standardized, a transformation which often improves performance.

exp_weight_lambda

A numerical value between 0 and 1. This parameter controls the exponential weighting of the Hermite series based estimator. If this parameter is NA, no exponential weighting is applied.

est_type

A string value. Options are "univariate" or "bivariate".

Value

An S3 object of class hermite_estimator_univar or hermite_estimator_bivar.

Author(s)

Michael Stephanou <michael.stephanou@gmail.com>

Examples

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hermite_est <- hermite_estimator(N = 30, standardize = TRUE,
est_type="univariate")

hermiter documentation built on Nov. 17, 2021, 1:07 a.m.