Empirical Mode Decomposition for Cyclostratigraphy
Tools to apply Ensemble Empirical Mode
Decomposition (EEMD) for cyclostratigraphy purposes. Mainly: a new
algorithm, extricate, that performs EEMD in seconds, a linear interpolation
algorithm  using the greatest rational common divisor of depth or time,
different algorithms to compute instantaneous amplitude, frequency and
ratios of frequencies, and functions to verify and visualise the outputs.
The functions were developed during the CRASH project (Checking the
Reproducibility of Astrochronology in the Hauterivian). When using for
publication please cite Wouters, S., Da Silva, A.C. Crucifix, M., Sinnesael,
M., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2019, "Litholog
generation with the 'StratigrapheR' package and signal decomposition for
cyclostratigraphic purposes". Geophysical Research Abstracts Vol. 21,
EGU2019-5520, 2019, EGU General Assembly 2019.
<http://hdl.handle.net/2268/234402>.
graphics, grDevices, stats, utils, StratigrapheR (>= 1.0.0),
hht, grid, hexbin, colorRamps, dplyr

graphics, stats, utils, StratigrapheR (>= 1.0.1), hht, grid,
hexbin, colorRamps, dplyr

graphics, stats, utils, StratigrapheR (>= 1.0.1), hht, grid,
hexbin, colorRamps, dplyr (>= 1.0.0)

graphics, stats, utils, StratigrapheR (>= 1.1.1), hht, grid,
hexbin, colorRamps, dplyr (>= 1.0.0)

graphics, stats, utils, usethis, tictoc, StratigrapheR (>=
1.1.1), hht, grid, hexbin, colorRamps, dplyr (>= 1.0.0)

Matthias Bogaert, Michel Ballins and Dirk Van den Poel
DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
of Artificial Training Examples) builds an ensemble of J48 trees by recursively
adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005). Creating diversity in ensembles using artificial data. Information Fusion, 6(1), 99-111. <doi:10.1016/j.inffus.2004.04.001>").

Matthias Bogaert, Michel Ballings and Dirk Van den Poel

DECORATE (Diverse Ensemble Creation by Oppositional Relabeling
of Artificial Training Examples) builds an ensemble of J48 trees by recursively
adding artificial samples of the training data ("Melville, P., & Mooney, R. J. (2005) <DOI:10.1016/j.inffus.2004.04.001>").