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PPMiss (version 0.1.2)

Copula-Based Estimator for Long-Range Dependent Processes under Missing Data

Description

Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) . Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in ) and has been found to outperform several other commonly applied estimators.

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Version

Install

install.packages('PPMiss')

Monthly Downloads

131

Version

0.1.2

License

GPL (>= 3)

Maintainer

Taiane Schaedler Prass

Last Published

February 18th, 2026

Functions in PPMiss (0.1.2)

PPMiss.Package

PPMiss: Copula-Based Estimator for Long-Range Dependent Processes under Missing Data.
arfima.coefs

Coefficients of an ARFIMA(p,d,q) model
k1fun

Constant K1
d.fit

Long memory parameter estimation
kdens

Kernel density estimator
PPMiss.copulas

Copula functions and the corresponding derivative limit.