{ "_id": "6ae19f968d250e7ad09ed53d", "Package": "VisualDom", "Type": "Package", "Title": "Visualize Dominant Variables in Wavelet Multiple Correlation", "Version": "0.8.0", "Authors@R": "c(person(given = \"Josué M.\", family = \"Polanco-Martínez\", role = c(\"aut\", \"cph\", \"cre\"), email = \"josue.m.polanco@gmail.com\", comment = c(ORCID = \"0000-0001-7164-0185\")))", "Maintainer": "Josué M. Polanco-Martínez ", "Description": "Estimates and plots as a heat map the correlation\ncoefficients obtained via the wavelet local multiple\ncorrelation 'WLMC' (Fernández-Macho 2018) and the 'dominant'\nvariable/s, i.e., the variable/s that maximizes the multiple\ncorrelation through time and scale (Polanco-Martínez et al.\n2020, Polanco-Martínez 2022). We improve the graphical outputs\nof WLMC proposing a didactic and useful way to visualize the\n'dominant' variable(s) for a set of time series. The WLMC was\ndesigned for financial time series, but other kinds of data\n(e.g., climatic, ecological, etc.) can be used. The functions\ncontained in 'VisualDom' are highly flexible since these\ncontains several parameters to personalize the time series\nunder analysis and the heat maps. In addition, we have also\nincluded two data sets (named 'rdata_climate' and\n'rdata_Lorenz') to exemplify the use of the functions contained\nin 'VisualDom'. Methods derived from Fernández-Macho (2018)\n, Polanco-Martínez et al.\n(2020) and Polanco-Martínez\n(2023, in press).", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "NeedsCompilation": "yes", "Repository": "https://jomopo.r-universe.dev", "Date/Publication": "2023-03-03 16:03:50 UTC", "RemoteUrl": "https://github.com/jomopo/visualdom", "RemoteRef": "HEAD", "RemoteSha": "922c79dc5f324664f84c7453fda762e50f878dcc", "Packaged": { "Date": "2026-10-26 11:52:46 UTC", "User": "root" }, "Author": "Josué M. 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