Package: DeCAFS 3.3.3

DeCAFS: Detecting Changes in Autocorrelated and Fluctuating Signals

Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.

Authors:Gaetano Romano [aut, cre], Guillem Rigaill [aut], Vincent Runge [aut], Paul Fearnhead [aut]

DeCAFS_3.3.3.tar.gz
DeCAFS_3.3.3.zip(r-4.5)DeCAFS_3.3.3.zip(r-4.4)DeCAFS_3.3.3.zip(r-4.3)
DeCAFS_3.3.3.tgz(r-4.4-x86_64)DeCAFS_3.3.3.tgz(r-4.4-arm64)DeCAFS_3.3.3.tgz(r-4.3-x86_64)DeCAFS_3.3.3.tgz(r-4.3-arm64)
DeCAFS_3.3.3.tar.gz(r-4.5-noble)DeCAFS_3.3.3.tar.gz(r-4.4-noble)
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DeCAFS.pdf |DeCAFS.html✨
DeCAFS/json (API)
NEWS

# Install 'DeCAFS' in R:

install.packages('DeCAFS', repos = c('https://gtromano.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gtromano/decafs/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

oilWell - Rock structure data from an oil well

On CRAN:

change-detection changepoint-detection time-series-analysis

3.00 score 2 stars 2 scripts 197 downloads 11 exports 31 dependencies

Last updated 2 years agofrom:2b3bd661ad. Checks:OK: 1 NOTE: 8. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 27 2024
R-4.5-win-x86_64	NOTE	Oct 27 2024
R-4.5-linux-x86_64	NOTE	Oct 27 2024
R-4.4-win-x86_64	NOTE	Oct 27 2024
R-4.4-mac-x86_64	NOTE	Oct 27 2024
R-4.4-mac-aarch64	NOTE	Oct 27 2024
R-4.3-win-x86_64	NOTE	Oct 27 2024
R-4.3-mac-x86_64	NOTE	Oct 27 2024
R-4.3-mac-aarch64	NOTE	Oct 27 2024

Exports:bestParameters cost dataRWAR dataSinusoidal DeCAFS estimateParameters estimVar evalEtaNu guidedModelSelection plot.DeCAFSout scenarioGenerator

Dependencies:cli colorspace DEoptimR fansi farver ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig R6 RColorBrewer Rcpp rlang robustbase scales tibble utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
bestParameters	bestParameters
L2 error estimation	cost
Generate a Random Walk + AR realization	dataRWAR
Generating data from a sinusoidal model with changes	dataSinusoidal
Main DeCAFS algorithm for detecting abrupt changes	DeCAFS
Estimate parameter in the Random Walk Autoregressive model	estimateParameters
Variance estimation for diff k operators	estimVar
RW and AR(1) variance estimations with fixed AR(1) parameter	evalEtaNu
Guided Model Selection	guidedModelSelection
Rock structure data from an oil well	oilWell
DeCAFS Plotting	plot.DeCAFSout
Generate a piecewise constant signal of a given length	scenarioGenerator