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 |
| CRAN checks: |
EFDR results |
Documentation:
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