{ "_id": "6a23e82b530b9bc726be9568", "Package": "RolWinMulCor", "Type": "Package", "Title": "Subroutines to Estimate Rolling Window Multiple Correlation", "Version": "1.2.0", "Authors@R": "c(person(given = \"Josue M.\", family = \"Polanco-Martinez\", role = c(\"aut\", \"cph\", \"cre\"), email = \"josue.m.polanco@gmail.com\", comment = c(ORCID = \"0000-0001-7164-0185\")))", "Maintainer": "Josue M. Polanco-Martinez ", "Description": "Rolling Window Multiple Correlation ('RolWinMulCor')\nestimates the rolling (running) window correlation for the bi-\nand multi-variate cases between regular (sampled on identical\ntime points) time series, with especial emphasis to ecological\ndata although this can be applied to other kinds of data sets.\n'RolWinMulCor' is based on the concept of rolling, running or\nsliding window and is useful to evaluate the evolution of\ncorrelation through time and time-scales. 'RolWinMulCor'\ncontains six functions. The first two focus on the bi-variate\ncase: (1) rolwincor_1win() and (2) rolwincor_heatmap(), which\nestimate the correlation coefficients and the their respective\np-values for only one window-length (time-scale) and\nconsidering all possible window-lengths or a band of\nwindow-lengths, respectively. The second two functions: (3)\nrolwinmulcor_1win() and (4) rolwinmulcor_heatmap() are designed\nto analyze the multi-variate case, following the bi-variate\ncase to visually display the results, but these two approaches\nare methodologically different. That is, the multi-variate case\nestimates the adjusted coefficients of determination instead of\nthe correlation coefficients. The last two functions: (5)\nplot_1win() and (6) plot_heatmap() are used to represent\ngraphically the outputs of the four aforementioned functions as\nsimple plots or as heat maps. The functions contained in\n'RolWinMulCor' are highly flexible since these contains several\nparameters to control the estimation of correlation and the\nfeatures of the plot output, e.g. to remove the (linear) trend\ncontained in the time series under analysis, to choose\ndifferent p-value correction methods (which are used to address\nthe multiple comparison problem) or to personalise the plot\noutputs. The 'RolWinMulCor' package also provides examples with\nsynthetic and real-life ecological time series to exemplify its\nuse. Methods derived from H. Abdi. (2007)\n,\nR. Telford (2013)\n, and\nJ. M. Polanco-Martinez (2020)\n.", "License": "GPL (>= 2)", "Encoding": "UTF-8", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-06 09:25:44 UTC", "User": "root" }, "Author": "Josue M. 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