Empirical Mode Decomposition and Hilbert Spectral Analysis
This package carries out empirical mode decompostion and Hilbert spectral
analysis.

This package carries out empirical mode decomposition and
Hilbert spectral analysis.

This package carries out empirical mode decomposition and
Hilbert spectral analysis. For usage of EMD, see Kim and Oh,
2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical
Mode Decomposition and Hilbert Spectrum, The R Journal, 1,
40-46).

This package carries out empirical mode decomposition and Hilbert spectral
analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical
Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

c(person("Donghoh", "Kim", role = c("aut", "cre"), email = "donghoh.kim@gmail.com"),
person("Hee-Seok", "Oh", role = "aut", email = "heeseok@stats.snu.ac.kr"))
Donghoh Kim [aut, cre],
Hee-Seok Oh [aut]
For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral
analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical
Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

c(person("Pankaj", "Das", role = c("aut","cre"),email="pankaj.das2@icar.gov.in"),person("Achal", "Lama", role = "aut",email="achal.lama@icar.gov.in"),
person("Girish", "Jha", role = "aut",email="grish.stat@gmail.com"))
Pankaj Das [aut, cre],
Achal Lama [aut],
Girish Jha [aut]
The researchers can use this package to fit Empirical Mode Decomposition and Artificial Neural Network based hybrid model for nonlinear and non stationary time series data.

Researchers can fit Empirical Mode Decomposition and Support Vector Regression based hybrid model for nonlinear and non stationary time series data using this package.

Earth Mover's Distance for Differential Analysis of Genomics
Data
The EMDomics algorithm is used to perform a supervised two-class
analysis to measure the magnitude and statistical significance of observed
continuous genomics data between two groups. Usually the data will be gene
expression values from array-based or sequence-based experiments, but data
from other types of experiments can also be analyzed (e.g. copy number
variation). Traditional methods like Significance Analysis of Microarrays
(SAM) and Linear Models for Microarray Data (LIMMA) use significance tests
based on summary statistics (mean and standard deviation) of the two
distributions. This approach lacks power to identify expression differences
between groups that show high levels of intra-group heterogeneity. The Earth
Mover's Distance (EMD) algorithm instead computes the "work" needed to
transform one distribution into the other, thus providing a metric of the
overall difference in shape between two distributions. Permutation of sample
labels is used to generate q-values for the observed EMD scores.
c(
person("Daniel", "Schmolze",
email="emd@schmolze.com", role=c("aut", "cre")),
person("Andrew", "Beck",
email="abeck2@bidmc.harvard.edu", role="aut"),
person("Sheida", "Nabavi",
email="sheida_nabavi@hms.harvard.edu", role="aut"))
Daniel Schmolze [aut, cre],
Andrew Beck [aut],
Sheida Nabavi [aut]