Package: EFDR
Type: Package
Title: Wavelet-Based Enhanced FDR for Signal Detection in Noisy Images
Version: 0.1.1
Date: 2015-04-03
Authors@R: c(person("Andrew", "Zammit-Mangion", role = c("aut", "cre"),
email = "andrewzm@gmail.com"),
person("Hsin-Cheng", "Huang", role = "aut"))
Suggests: knitr, ggplot2, RCurl, fields, gridExtra, animation
Description: 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.
Imports: Matrix, 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)
URL: http://github.com/andrewzm/EFDR
Depends: R (>= 3.0)
VignetteBuilder: knitr
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2015-04-04 07:37:08 UTC; andrew
Author: Andrew Zammit-Mangion [aut, cre],
Hsin-Cheng Huang [aut]
Maintainer: Andrew Zammit-Mangion <andrewzm@gmail.com>
Repository: CRAN
Date/Publication: 2015-04-04 13:33:23

Title: Wavelet-Based Enhanced FDR for Detecting Signals from Complete
Imports: copula, Matrix, methods, foreach (>= 1.4.2), doParallel (>=

Suggests: knitr, rmarkdown, markdown, ggplot2, RCurl, fields,

Title: Tool for Ensemble Feature Selection
Description: Provides a function to check the
importance of a feature based on a dependent classification
variable. An ensemble of correlation and importance measure
tests are used to determine the normed importance value of
all features. Combining these methods in one function
(building the sum of the importance values) leads to a better
tool for selecting most important features. This selection
can also be viewed in a barplot using the barplot_fs() function
and proved using an also provided function for a logistic
Imports: party, pROC, randomForest, ROCR, grDevices, graphics, stats