EFDR: Wavelet-Based Enhanced FDR for Detecting Signals from Complete
or Incomplete Spatially Aggregated Data
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
in an image. The image is first transformed into the wavelet ___domain in
order to decorrelate any noise components, following which the coefficients
at each resolution are standardised. Statistical tests (in a multiple
hypothesis testing setting) are then carried out to find the anomalies. The
power of EFDR exceeds that of standard FDR, which would carry out tests on
every wavelet coefficient: EFDR choose which wavelets to test based on a
criterion described in Shen et al. (2002). The package also provides
elementary tools to interpolate spatially irregular data onto a grid of the
required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
'Nonparametric hypothesis testing for a spatial signal.' Journal of the
American Statistical Association 97.460 (2002): 1122-1140.
| Version: |
1.3 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
copula, Matrix, methods, foreach (≥ 1.4.2), doParallel (≥
1.0.8), waveslim (≥ 1.7.5), parallel, gstat (≥ 1.0-19), tidyr (≥ 0.1.0.9000), dplyr (≥ 0.3.0.2), sp (≥ 1.0-15) |
| Suggests: |
knitr, rmarkdown, markdown, ggplot2, RCurl, fields, gridExtra, animation |
| Published: |
2023-08-22 |
| DOI: |
10.32614/CRAN.package.EFDR |
| Author: |
Andrew Zammit-Mangion [aut, cre],
Hsin-Cheng Huang [aut] |
| Maintainer: |
Andrew Zammit-Mangion <andrewzm at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/andrewzm/EFDR/ |
| NeedsCompilation: |
no |
| Citation: |
EFDR citation info |
| In views: |
AnomalyDetection |
| CRAN checks: |
EFDR results |
Documentation:
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