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

Implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. 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 <doi:10.48550/arXiv.2303.04754>) and has been found to outperform several other commonly applied estimators.

Version: 0.1.1
Depends: R (≥ 4.0.0)
Imports: copula, pracma, stats, zoo
Published: 2023-09-04
Author: Taiane Schaedler Prass ORCID iD [aut, cre, com], Guilherme Pumi ORCID iD [aut, ctb]
Maintainer: Taiane Schaedler Prass <taianeprass at gmail.com>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: NEWS
CRAN checks: PPMiss results

Documentation:

Reference manual: PPMiss.pdf

Downloads:

Package source: PPMiss_0.1.1.tar.gz
Windows binaries: r-devel: PPMiss_0.1.1.zip, r-release: PPMiss_0.1.1.zip, r-oldrel: PPMiss_0.1.1.zip
macOS binaries: r-release (arm64): PPMiss_0.1.1.tgz, r-oldrel (arm64): PPMiss_0.1.1.tgz, r-release (x86_64): PPMiss_0.1.1.tgz
Old sources: PPMiss archive

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