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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
 [aut, cre,
com],
Guilherme Pumi
[aut, ctb] |
| Maintainer: |
Taiane Schaedler Prass <taianeprass at gmail.com> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
PPMiss results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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