WaverideR: Extracting Signals from Wavelet Spectra
In WaverideR: Extracting Signals from Wavelet Spectra
WaverideR R Documentation
Extracting Signals from Wavelet Spectra
Description
WaverideR is an R package for advanced cyclostratigraphic
analysis of stratigraphic data sets. It provides a comprehensive suite
of spectral tools for detecting, visualising, and tracking non-stationary
astronomical cycles, including continuous wavelet and superlet transform (CWT),
the superlet transform, windowed FFT, and evolutionary harmonic
analysis (EHA) (wrapper). These methods allow both manual and automated
tracking of orbital cycles in spectra and scalograms, even in records
affected by large changes in sedimentation rate.Building on this
spectral framework, WaverideR supports multi-proxy integration and
Monte Carlo–based uncertainty propagation to construct statistically
robust floating and absolute astrochronological age models. The package
includes dedicated tools to quantify analytical wavelet uncertainty,
estimate the duration of stratigraphic gaps and hiatuses, and integrate
external radioisotopic age constraints. Designed for complex and
incomplete stratigraphic records, WaverideR enables investigation
of the imprint of astronomical forcing even in suboptimal datasets.
Details
Package: 'WaverideR'
Type: R package
Version: 0.5.0 (1st quarter 2026)
License: GPL (= 2)
Note
If you want to use this package for publication or research
purposes, please cite:
Arts, M.C.M (2023).
WaverideR: Extracting Signals from Wavelet Spectra.
https://CRAN.R-project.org/package=WaverideR
Author(s)
Michiel Arts
Maintainer: Michiel Arts michiel.arts@stratigraphy.eu
References
The 'WaverideR' package builds upon existing literature and existing codebase.
The following list of articles is relevant for the 'WaverideR' R package and
its functions. Individual articles are also cited in the descriptions of
function when relative for set function. The articles in the list below can
be grouped in four subjects: (1) Cyclostratigraphic data analysis, (2)
example data sets, (3) the (continuous) wavelet transform and (4)
astronomical solutions). For each of these categories the
relevance of set articles will be explained in the framework of
the 'WaverideR' R package.
# 1. Cyclostratigraphic data analysis
Meyers, S. R. (2019). Cyclostratigraphy and the problem of astrochronologic
testing. Earth-Science Reviews, 190, 190–223.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.earscirev.2018.11.015")}
Meyers, S. R. (2012). Seeing red in cyclic stratigraphy: Spectral noise
estimation for astrochronology. Paleoceanography, 27, PA3228.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1029/2012PA002307")}
Li, M., Hinnov, L. A., and Kump, L. R. (2019). Acycle: Time-series analysis
software for paleoclimate research and education. Computers and Geosciences,
127, 12–22. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.cageo.2019.02.011")}
Li, M., Kump, L. R., Hinnov, L. A., and Mann, M. E. (2018). Tracking variable
sedimentation rates and astronomical forcing in Phanerozoic paleoclimate proxy
series with evolutionary correlation coefficients and hypothesis testing.
Earth and Planetary Science Letters, 501, 165–179.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.epsl.2018.08.041")}
Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.-C., Zeeden, C.,
Zivanovic, M., Boulvain, F., and Devleeschouwer, X. (2022). A decomposition
approach to cyclostratigraphic signal processing. Earth-Science Reviews, 225,
103894. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.earscirev.2021.103894")}
Wouters, S., Da Silva, A.-C., Boulvain, F., and Devleeschouwer, X. (2021).
StratigrapheR: Concepts for litholog generation in R. The R Journal, 13.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2021-039")}
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., and Blank, K.
(2009).On instantaneous frequency. Advances in Adaptive Data Analysis,
1, 177–229. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1142/S1793536909000096")}
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing
scatterplots. Journal of the American Statistical Association, 74, 829–836.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1979.10481038")}
Hurvich, C. M., Simonoff, J. S., and Tsai, C.-L. (1998). Smoothing parameter
selection in nonparametric regression using an improved Akaike information
criterion. Journal of the Royal Statistical Society: Series B, 60, 271–293.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1467-9868.00125")}
Golub, G. H., Heath, M., and Wahba, G. (1979). Generalized cross-validation
as a method for choosing a good ridge parameter. Technometrics, 21, 215–224.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1268518")}
#2. Example data sets
Pas, D., Hinnov, L. A., Day, J. E., Kodama, K., Sinnesael, M., and Liu, W.
(2018). Cyclostratigraphic calibration of the Famennian Stage
(Late Devonian, Illinois Basin, USA). Earth and Planetary Science Letters,
488, 102–114. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.epsl.2018.02.010")}
Steinhilber, F., Abreu, J., Beer, J., Brunner, I., Christl, M., Fischer, H.,
Heikkilä, U., Kubik, P. W., Mann, M. E., McCracken, K. G., Miller, H., Miyahara,
H., Oerter, H., and Wilhelms, F. (2012). 9400 years of cosmic radiation and solar
activity from ice cores and tree rings. Proceedings of the National Academy of
Sciences of the United States of America, 109, 5967–5971.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.1118965109")}
Zeeden, C., Hilgen, F. J., Westerhold, T., Lourens, L. J., Röhl, U., and
Bickert, T. (2013). Revised Miocene splice, astronomical tuning and calcareous
plankton biochronology of ODP Site 926 between 5 and 14.4 Ma.
Palaeogeography, Palaeoclimatology, Palaeoecology, 369, 430–451.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.palaeo.2012.11.009")}
#3. Continuous wavelet and superlet transform
Moca, V. V., Bârzan, H., Nagy-Dăbâcan, A., and Mureșan, R. C. (2021).
Time-frequency super-resolution with superlets. Nature Communications, 12, 337.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41467-020-20539-9")}
Morlet, J., Arens, G., Fourgeau, E., and Giard, D. (1982a). Wave propagation and
sampling theory. Part I: Complex signal and scattering in multilayered media.
Geophysics, 47, 203–221.
Morlet, J., Arens, G., Fourgeau, E., and Giard, D. (1982b). Wave propagation and
sampling theory. Part II: Sampling theory and complex waves. Geophysics, 47,
222–236.
Torrence, C., and Compo, G. P. (1998). A practical guide to wavelet analysis.
Bulletin of the American Meteorological Society, 79, 61–78.
Gouhier, T. C., Grinsted, A., and Simko, V. (2021). biwavelet: Conduct univariate
and bivariate wavelet analyses. R package version 0.20.21.
Roesch, A., and Schmidbauer, H. (2018). WaveletComp: Computational wavelet
analysis. R package version 1.1.
Gabor, D. (1946). Theory of communication. Part I: The analysis of information.
Journal of the Institution of Electrical Engineers, Part III, 93, 429–441.
#4. Astronomical solutions
Laskar, J., Robutel, P., Joutel, F., Gastineau, M., Correia, A. C. M., and
Levrard, B. (2004). A long-term numerical solution for the insolation quantities
of the Earth. Astronomy and Astrophysics, 428, 261–285.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1051/0004-6361:20041335")}
Laskar, J., Fienga, A., Gastineau, M., and Manche, H. (2011a). La2010: A new
orbital solution for the long-term motion of the Earth. Astronomy and
Astrophysics, 532, A89. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1051/0004-6361/201116836")}
Zeebe, R. E., and Lourens, L. J. (2019). Solar System chaos and the
Paleocene–Eocene boundary age constrained by geology and astronomy.
Science, 365, 926–929. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.aax0612")}
WaverideR documentation built on April 6, 2026, 5:06 p.m.
R Package Documentation
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| WaverideR | R Documentation |
Extracting Signals from Wavelet Spectra
Description
WaverideR is an R package for advanced cyclostratigraphic analysis of stratigraphic data sets. It provides a comprehensive suite of spectral tools for detecting, visualising, and tracking non-stationary astronomical cycles, including continuous wavelet and superlet transform (CWT), the superlet transform, windowed FFT, and evolutionary harmonic analysis (EHA) (wrapper). These methods allow both manual and automated tracking of orbital cycles in spectra and scalograms, even in records affected by large changes in sedimentation rate.Building on this spectral framework, WaverideR supports multi-proxy integration and Monte Carlo–based uncertainty propagation to construct statistically robust floating and absolute astrochronological age models. The package includes dedicated tools to quantify analytical wavelet uncertainty, estimate the duration of stratigraphic gaps and hiatuses, and integrate external radioisotopic age constraints. Designed for complex and incomplete stratigraphic records, WaverideR enables investigation of the imprint of astronomical forcing even in suboptimal datasets.
Details
Package: 'WaverideR'
Type: R package
Version: 0.5.0 (1st quarter 2026)
License: GPL (= 2)
Note
If you want to use this package for publication or research purposes, please cite:
Arts, M.C.M (2023). WaverideR: Extracting Signals from Wavelet Spectra. https://CRAN.R-project.org/package=WaverideR
Author(s)
Michiel Arts
Maintainer: Michiel Arts michiel.arts@stratigraphy.eu
References
The 'WaverideR' package builds upon existing literature and existing codebase.
The following list of articles is relevant for the 'WaverideR' R package and
its functions. Individual articles are also cited in the descriptions of
function when relative for set function. The articles in the list below can
be grouped in four subjects: (1) Cyclostratigraphic data analysis, (2)
example data sets, (3) the (continuous) wavelet transform and (4)
astronomical solutions). For each of these categories the
relevance of set articles will be explained in the framework of
the 'WaverideR' R package.
# 1. Cyclostratigraphic data analysis
Meyers, S. R. (2019). Cyclostratigraphy and the problem of astrochronologic
testing. Earth-Science Reviews, 190, 190–223.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.earscirev.2018.11.015")}
Meyers, S. R. (2012). Seeing red in cyclic stratigraphy: Spectral noise
estimation for astrochronology. Paleoceanography, 27, PA3228.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1029/2012PA002307")}
Li, M., Hinnov, L. A., and Kump, L. R. (2019). Acycle: Time-series analysis
software for paleoclimate research and education. Computers and Geosciences,
127, 12–22. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.cageo.2019.02.011")}
Li, M., Kump, L. R., Hinnov, L. A., and Mann, M. E. (2018). Tracking variable
sedimentation rates and astronomical forcing in Phanerozoic paleoclimate proxy
series with evolutionary correlation coefficients and hypothesis testing.
Earth and Planetary Science Letters, 501, 165–179.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.epsl.2018.08.041")}
Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.-C., Zeeden, C.,
Zivanovic, M., Boulvain, F., and Devleeschouwer, X. (2022). A decomposition
approach to cyclostratigraphic signal processing. Earth-Science Reviews, 225,
103894. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.earscirev.2021.103894")}
Wouters, S., Da Silva, A.-C., Boulvain, F., and Devleeschouwer, X. (2021).
StratigrapheR: Concepts for litholog generation in R. The R Journal, 13.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2021-039")}
Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., and Blank, K.
(2009).On instantaneous frequency. Advances in Adaptive Data Analysis,
1, 177–229. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1142/S1793536909000096")}
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing
scatterplots. Journal of the American Statistical Association, 74, 829–836.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1979.10481038")}
Hurvich, C. M., Simonoff, J. S., and Tsai, C.-L. (1998). Smoothing parameter
selection in nonparametric regression using an improved Akaike information
criterion. Journal of the Royal Statistical Society: Series B, 60, 271–293.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/1467-9868.00125")}
Golub, G. H., Heath, M., and Wahba, G. (1979). Generalized cross-validation
as a method for choosing a good ridge parameter. Technometrics, 21, 215–224.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1268518")}
#2. Example data sets
Pas, D., Hinnov, L. A., Day, J. E., Kodama, K., Sinnesael, M., and Liu, W.
(2018). Cyclostratigraphic calibration of the Famennian Stage
(Late Devonian, Illinois Basin, USA). Earth and Planetary Science Letters,
488, 102–114. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.epsl.2018.02.010")}
Steinhilber, F., Abreu, J., Beer, J., Brunner, I., Christl, M., Fischer, H.,
Heikkilä, U., Kubik, P. W., Mann, M. E., McCracken, K. G., Miller, H., Miyahara,
H., Oerter, H., and Wilhelms, F. (2012). 9400 years of cosmic radiation and solar
activity from ice cores and tree rings. Proceedings of the National Academy of
Sciences of the United States of America, 109, 5967–5971.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.1118965109")}
Zeeden, C., Hilgen, F. J., Westerhold, T., Lourens, L. J., Röhl, U., and
Bickert, T. (2013). Revised Miocene splice, astronomical tuning and calcareous
plankton biochronology of ODP Site 926 between 5 and 14.4 Ma.
Palaeogeography, Palaeoclimatology, Palaeoecology, 369, 430–451.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.palaeo.2012.11.009")}
#3. Continuous wavelet and superlet transform
Moca, V. V., Bârzan, H., Nagy-Dăbâcan, A., and Mureșan, R. C. (2021).
Time-frequency super-resolution with superlets. Nature Communications, 12, 337.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41467-020-20539-9")}
Morlet, J., Arens, G., Fourgeau, E., and Giard, D. (1982a). Wave propagation and
sampling theory. Part I: Complex signal and scattering in multilayered media.
Geophysics, 47, 203–221.
Morlet, J., Arens, G., Fourgeau, E., and Giard, D. (1982b). Wave propagation and
sampling theory. Part II: Sampling theory and complex waves. Geophysics, 47,
222–236.
Torrence, C., and Compo, G. P. (1998). A practical guide to wavelet analysis.
Bulletin of the American Meteorological Society, 79, 61–78.
Gouhier, T. C., Grinsted, A., and Simko, V. (2021). biwavelet: Conduct univariate
and bivariate wavelet analyses. R package version 0.20.21.
Roesch, A., and Schmidbauer, H. (2018). WaveletComp: Computational wavelet
analysis. R package version 1.1.
Gabor, D. (1946). Theory of communication. Part I: The analysis of information.
Journal of the Institution of Electrical Engineers, Part III, 93, 429–441.
#4. Astronomical solutions
Laskar, J., Robutel, P., Joutel, F., Gastineau, M., Correia, A. C. M., and
Levrard, B. (2004). A long-term numerical solution for the insolation quantities
of the Earth. Astronomy and Astrophysics, 428, 261–285.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1051/0004-6361:20041335")}
Laskar, J., Fienga, A., Gastineau, M., and Manche, H. (2011a). La2010: A new
orbital solution for the long-term motion of the Earth. Astronomy and
Astrophysics, 532, A89. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1051/0004-6361/201116836")}
Zeebe, R. E., and Lourens, L. J. (2019). Solar System chaos and the Paleocene–Eocene boundary age constrained by geology and astronomy. Science, 365, 926–929. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.aax0612")}
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